�ݺ�ߣshows by User: skyl

�ݺ�ߣshows by User: skyl_ai / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: skyl_ai / Fri, 09 Apr 2021 07:26:58 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: skyl_ai How to perform Secure Data Labeling for Machine Learning /slideshow/how-to-perform-secure-data-labeling-for-machine-learning/245952273 howtoperformsecuredatalabelingformachinelearning-210409072658
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling]]>
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling]]> Fri, 09 Apr 2021 07:26:58 GMT /slideshow/how-to-perform-secure-data-labeling-for-machine-learning/245952273 skyl_ai@slideshare.net(skyl_ai) How to perform Secure Data Labeling for Machine Learning skyl_ai Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtoperformsecuredatalabelingformachinelearning-210409072658-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling

How to perform Secure Data Labeling for Machine Learning from Skyl.ai

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0 AI in Quality Control: How to perform Visual Inspection with AI /slideshow/ai-in-quality-control-how-to-perform-visual-inspection-with-ai/244277145 aiinqualitycontrolhowtoperformvisualinspectionwithai-210312083420
About the webinar: Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc., are employing AI-powered solutions to detect defects early and avoid defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn: - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label, and train the AI model to detect defects, all within a few minutes.]]>
About the webinar: Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc., are employing AI-powered solutions to detect defects early and avoid defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn: - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label, and train the AI model to detect defects, all within a few minutes.]]> Fri, 12 Mar 2021 08:34:19 GMT /slideshow/ai-in-quality-control-how-to-perform-visual-inspection-with-ai/244277145 skyl_ai@slideshare.net(skyl_ai) AI in Quality Control: How to perform Visual Inspection with AI skyl_ai About the webinar: Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc., are employing AI-powered solutions to detect defects early and avoid defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn: - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label, and train the AI model to detect defects, all within a few minutes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiinqualitycontrolhowtoperformvisualinspectionwithai-210312083420-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar: Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc., are employing AI-powered solutions to detect defects early and avoid defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn: - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label, and train the AI model to detect defects, all within a few minutes.

AI in Quality Control: How to perform Visual Inspection with AI from Skyl.ai

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0 How to analyze text data with Named Entity Recognition /skyl_ai/how-to-analyze-text-data-with-named-entity-recognition howtoanalyzetextdatawithnamedentityrecognition-210212075650
The internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents, analyze sentiments, improving automatically generated summaries, etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data. What you will learn: - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months]]>
The internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents, analyze sentiments, improving automatically generated summaries, etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data. What you will learn: - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months]]> Fri, 12 Feb 2021 07:56:49 GMT /skyl_ai/how-to-analyze-text-data-with-named-entity-recognition skyl_ai@slideshare.net(skyl_ai) How to analyze text data with Named Entity Recognition skyl_ai The internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents, analyze sentiments, improving automatically generated summaries, etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data. What you will learn: - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtoanalyzetextdatawithnamedentityrecognition-210212075650-thumbnail.jpg?width=120&height=120&fit=bounds" /> The internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents, analyze sentiments, improving automatically generated summaries, etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data. What you will learn: - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months

How to analyze text data with Named Entity Recognition from Skyl.ai

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0 How to do Secure Data Labeling for Machine Learning /slideshow/how-to-do-secure-data-labeling-for-machine-learning-239110376/239110376 howtodosecuredatalabelingformachinelearning-201106050634
Data annotation or more commonly called data labeling is an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is about handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform]]>
Data annotation or more commonly called data labeling is an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is about handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform]]> Fri, 06 Nov 2020 05:06:34 GMT /slideshow/how-to-do-secure-data-labeling-for-machine-learning-239110376/239110376 skyl_ai@slideshare.net(skyl_ai) How to do Secure Data Labeling for Machine Learning skyl_ai Data annotation or more commonly called data labeling is an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is about handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtodosecuredatalabelingformachinelearning-201106050634-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data annotation or more commonly called data labeling is an integral part of AI and Machine Learning. One of the biggest concerns that organizations have while doing AI and ML is about handling data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn: - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform

How to do Secure Data Labeling for Machine Learning from Skyl.ai

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0 No Code AI - How to Deploy Machine Learning Models with Zero Code? /slideshow/no-code-ai-how-to-deploy-machine-learning-models-with-zero-code-238739103/238739103 nocodeai-howtodeploymachinelearningmodelswithzerocode-201005065043
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives. Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects. What you will learn: - Traditional vs No Code AI Process - Best practices to accelerate machine learning adoption - Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks]]>
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives. Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects. What you will learn: - Traditional vs No Code AI Process - Best practices to accelerate machine learning adoption - Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks]]> Mon, 05 Oct 2020 06:50:43 GMT /slideshow/no-code-ai-how-to-deploy-machine-learning-models-with-zero-code-238739103/238739103 skyl_ai@slideshare.net(skyl_ai) No Code AI - How to Deploy Machine Learning Models with Zero Code? skyl_ai In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives. Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects. What you will learn: - Traditional vs No Code AI Process - Best practices to accelerate machine learning adoption - Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nocodeai-howtodeploymachinelearningmodelswithzerocode-201005065043-thumbnail.jpg?width=120&height=120&fit=bounds" /> In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives. Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects. What you will learn: - Traditional vs No Code AI Process - Best practices to accelerate machine learning adoption - Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks

No Code AI - How to Deploy Machine Learning Models with Zero Code? from Skyl.ai

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0 AI in Insurance: How to Automate Insurance Claims Processing with Machine Learning /slideshow/ai-in-insurance-how-to-automate-insurance-claims-processing-with-machine-learning-238392112/238392112 aiininsurancehowtoautomateinsuranceclaimsprocessingwithmachinelearning-200904093001
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support teams, and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system, and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you will learn: . Deep dive into how insurance companies are adopting AI . Discuss prominent industry use cases . Live demo of vehicle damage assessment for insurance claims management]]>
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support teams, and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system, and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you will learn: . Deep dive into how insurance companies are adopting AI . Discuss prominent industry use cases . Live demo of vehicle damage assessment for insurance claims management]]> Fri, 04 Sep 2020 09:30:01 GMT /slideshow/ai-in-insurance-how-to-automate-insurance-claims-processing-with-machine-learning-238392112/238392112 skyl_ai@slideshare.net(skyl_ai) AI in Insurance: How to Automate Insurance Claims Processing with Machine Learning skyl_ai Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support teams, and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system, and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you will learn: . Deep dive into how insurance companies are adopting AI . Discuss prominent industry use cases . Live demo of vehicle damage assessment for insurance claims management <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiininsurancehowtoautomateinsuranceclaimsprocessingwithmachinelearning-200904093001-thumbnail.jpg?width=120&height=120&fit=bounds" /> Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support teams, and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system, and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you will learn: . Deep dive into how insurance companies are adopting AI . Discuss prominent industry use cases . Live demo of vehicle damage assessment for insurance claims management

AI in Insurance: How to Automate Insurance Claims Processing with Machine Learning from Skyl.ai

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0 Solving the dilemma should you build or buy ai /skyl_ai/solving-the-dilemma-should-you-build-or-buy-ai-237650409 solvingthedilemmashouldyoubuildorbuyai-200807163357
About the webinar Long gone are the days of questioning if your organization requires Artificial Intelligence to drive competitive advantage. 84% of businesses say AI will enable them to obtain or sustain the competitive advantage [Forbes]. AI offers highly data-driven insights, automates mundane processes, enhances customer experience, and hence increases overall efficiency. 36% of executives say their primary goal for AI is to free up workers to be more creative by automating tasks [HBR]. Businesses know what AI solutions they need but the real challenge lies in getting them implemented. AI initiatives require proper evaluation of the organizations’ ability to build in-house AI technology or buy commercially available AI applications. Through this webinar, you will know the factors to consider while making the decision of AI implementation, and you get the answer to your biggest question - whether to build or buy AI application. What you will learn What factors to evaluate before making a decision to build or buy an AI solution What will you require to build an AI model specific to your organizational need How does building an AI solution fit into the long-term business model and help in gaining competitive advantage]]>
About the webinar Long gone are the days of questioning if your organization requires Artificial Intelligence to drive competitive advantage. 84% of businesses say AI will enable them to obtain or sustain the competitive advantage [Forbes]. AI offers highly data-driven insights, automates mundane processes, enhances customer experience, and hence increases overall efficiency. 36% of executives say their primary goal for AI is to free up workers to be more creative by automating tasks [HBR]. Businesses know what AI solutions they need but the real challenge lies in getting them implemented. AI initiatives require proper evaluation of the organizations’ ability to build in-house AI technology or buy commercially available AI applications. Through this webinar, you will know the factors to consider while making the decision of AI implementation, and you get the answer to your biggest question - whether to build or buy AI application. What you will learn What factors to evaluate before making a decision to build or buy an AI solution What will you require to build an AI model specific to your organizational need How does building an AI solution fit into the long-term business model and help in gaining competitive advantage]]> Fri, 07 Aug 2020 16:33:57 GMT /skyl_ai/solving-the-dilemma-should-you-build-or-buy-ai-237650409 skyl_ai@slideshare.net(skyl_ai) Solving the dilemma should you build or buy ai skyl_ai About the webinar Long gone are the days of questioning if your organization requires Artificial Intelligence to drive competitive advantage. 84% of businesses say AI will enable them to obtain or sustain the competitive advantage [Forbes]. AI offers highly data-driven insights, automates mundane processes, enhances customer experience, and hence increases overall efficiency. 36% of executives say their primary goal for AI is to free up workers to be more creative by automating tasks [HBR]. Businesses know what AI solutions they need but the real challenge lies in getting them implemented. AI initiatives require proper evaluation of the organizations’ ability to build in-house AI technology or buy commercially available AI applications. Through this webinar, you will know the factors to consider while making the decision of AI implementation, and you get the answer to your biggest question - whether to build or buy AI application. What you will learn What factors to evaluate before making a decision to build or buy an AI solution What will you require to build an AI model specific to your organizational need How does building an AI solution fit into the long-term business model and help in gaining competitive advantage <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/solvingthedilemmashouldyoubuildorbuyai-200807163357-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar Long gone are the days of questioning if your organization requires Artificial Intelligence to drive competitive advantage. 84% of businesses say AI will enable them to obtain or sustain the competitive advantage [Forbes]. AI offers highly data-driven insights, automates mundane processes, enhances customer experience, and hence increases overall efficiency. 36% of executives say their primary goal for AI is to free up workers to be more creative by automating tasks [HBR]. Businesses know what AI solutions they need but the real challenge lies in getting them implemented. AI initiatives require proper evaluation of the organizations’ ability to build in-house AI technology or buy commercially available AI applications. Through this webinar, you will know the factors to consider while making the decision of AI implementation, and you get the answer to your biggest question - whether to build or buy AI application. What you will learn What factors to evaluate before making a decision to build or buy an AI solution What will you require to build an AI model specific to your organizational need How does building an AI solution fit into the long-term business model and help in gaining competitive advantage

Solving the dilemma should you build or buy ai from Skyl.ai

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0 How AI and Machine Learning can Transform Organizations /slideshow/how-ai-and-machine-learning-can-transform-organizations/237186834 howaiandmachinelearningcantransformorganizations-200723155607
About the webinar 83% of businesses say AI is a strategic priority for their businesses today, while only 23% of businesses have incorporated AI into processes and product/service offerings today [source: Forbes]. Artificial intelligence and machine learning have started to disrupt the traditional way of doing business and revolutionize everything from farming to rocket science. Do you want to be left behind? Through this webinar, we will discover how various industries are adopting technologies to innovate and disrupt their business models to increase revenue, reduce costs, improve quality and customer satisfaction as well as to handle risks. What you will learn - How organizations have gained benefits with AI in their business to increase revenue, reduce cost, improve quality and manage risks - Mind-blowing Innovative and disruptive emerging AI use cases in various industry sectors - How to leverage AI in your business to get a competitive advantage]]>
About the webinar 83% of businesses say AI is a strategic priority for their businesses today, while only 23% of businesses have incorporated AI into processes and product/service offerings today [source: Forbes]. Artificial intelligence and machine learning have started to disrupt the traditional way of doing business and revolutionize everything from farming to rocket science. Do you want to be left behind? Through this webinar, we will discover how various industries are adopting technologies to innovate and disrupt their business models to increase revenue, reduce costs, improve quality and customer satisfaction as well as to handle risks. What you will learn - How organizations have gained benefits with AI in their business to increase revenue, reduce cost, improve quality and manage risks - Mind-blowing Innovative and disruptive emerging AI use cases in various industry sectors - How to leverage AI in your business to get a competitive advantage]]> Thu, 23 Jul 2020 15:56:07 GMT /slideshow/how-ai-and-machine-learning-can-transform-organizations/237186834 skyl_ai@slideshare.net(skyl_ai) How AI and Machine Learning can Transform Organizations skyl_ai About the webinar 83% of businesses say AI is a strategic priority for their businesses today, while only 23% of businesses have incorporated AI into processes and product/service offerings today [source: Forbes]. Artificial intelligence and machine learning have started to disrupt the traditional way of doing business and revolutionize everything from farming to rocket science. Do you want to be left behind? Through this webinar, we will discover how various industries are adopting technologies to innovate and disrupt their business models to increase revenue, reduce costs, improve quality and customer satisfaction as well as to handle risks. What you will learn - How organizations have gained benefits with AI in their business to increase revenue, reduce cost, improve quality and manage risks - Mind-blowing Innovative and disruptive emerging AI use cases in various industry sectors - How to leverage AI in your business to get a competitive advantage <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howaiandmachinelearningcantransformorganizations-200723155607-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar 83% of businesses say AI is a strategic priority for their businesses today, while only 23% of businesses have incorporated AI into processes and product/service offerings today [source: Forbes]. Artificial intelligence and machine learning have started to disrupt the traditional way of doing business and revolutionize everything from farming to rocket science. Do you want to be left behind? Through this webinar, we will discover how various industries are adopting technologies to innovate and disrupt their business models to increase revenue, reduce costs, improve quality and customer satisfaction as well as to handle risks. What you will learn - How organizations have gained benefits with AI in their business to increase revenue, reduce cost, improve quality and manage risks - Mind-blowing Innovative and disruptive emerging AI use cases in various industry sectors - How to leverage AI in your business to get a competitive advantage

How AI and Machine Learning can Transform Organizations from Skyl.ai

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0 How to do Secure Data Labeling for Machine Learning /slideshow/how-to-do-secure-data-labeling-for-machine-learning/236756742 howtodosecuredatalabelingformachinelearning-200709155457
About the webinar Data annotations or more commonly called data labeling is an integral part of AI and Machine learning. One of the biggest concerns that organizations have while doing AI and ML is about handing data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform]]>
About the webinar Data annotations or more commonly called data labeling is an integral part of AI and Machine learning. One of the biggest concerns that organizations have while doing AI and ML is about handing data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform]]> Thu, 09 Jul 2020 15:54:56 GMT /slideshow/how-to-do-secure-data-labeling-for-machine-learning/236756742 skyl_ai@slideshare.net(skyl_ai) How to do Secure Data Labeling for Machine Learning skyl_ai About the webinar Data annotations or more commonly called data labeling is an integral part of AI and Machine learning. One of the biggest concerns that organizations have while doing AI and ML is about handing data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtodosecuredatalabelingformachinelearning-200709155457-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar Data annotations or more commonly called data labeling is an integral part of AI and Machine learning. One of the biggest concerns that organizations have while doing AI and ML is about handing data. Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount. What you will learn - Risks associated with data annotations and how to manage data privacy and data protection - How to handle deployments and infrastructure to manage data security - How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling - Live demo of a secure data labeling platform

How to do Secure Data Labeling for Machine Learning from Skyl.ai

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0 Twitter Sentiment Analysis in 10 Minutes using Machine Learning /slideshow/twitter-sentiment-analysis-in-10-minutes-using-machine-learning-236211016/236211016 twittersentimentanalysisin10minutesusingmachinelearning-200625155613
About the webinar: Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook. This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. What you will learn - How businesses are leveraging sentiment analysis to their advantage - Best practice to automate machine learning models in hours not months - Demo: How to build a twitter sentiment analysis model]]>
About the webinar: Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook. This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. What you will learn - How businesses are leveraging sentiment analysis to their advantage - Best practice to automate machine learning models in hours not months - Demo: How to build a twitter sentiment analysis model]]> Thu, 25 Jun 2020 15:56:13 GMT /slideshow/twitter-sentiment-analysis-in-10-minutes-using-machine-learning-236211016/236211016 skyl_ai@slideshare.net(skyl_ai) Twitter Sentiment Analysis in 10 Minutes using Machine Learning skyl_ai About the webinar: Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook. This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. What you will learn - How businesses are leveraging sentiment analysis to their advantage - Best practice to automate machine learning models in hours not months - Demo: How to build a twitter sentiment analysis model <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/twittersentimentanalysisin10minutesusingmachinelearning-200625155613-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar: Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook. This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. What you will learn - How businesses are leveraging sentiment analysis to their advantage - Best practice to automate machine learning models in hours not months - Demo: How to build a twitter sentiment analysis model

Twitter Sentiment Analysis in 10 Minutes using Machine Learning from Skyl.ai

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0 How to classify documents automatically using NLP /slideshow/how-to-classify-documents-automatically-using-nlp/235854255 howtoclassifydocumentsautomaticallyusingnlp-200618161357
About the webinar Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business. Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts. In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc. What you will learn - How businesses are leveraging document classification to their advantage - Best practice to automate machine learning models in hours not months - Demo: Classify news articles into the right category using convolution neural network]]>
About the webinar Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business. Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts. In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc. What you will learn - How businesses are leveraging document classification to their advantage - Best practice to automate machine learning models in hours not months - Demo: Classify news articles into the right category using convolution neural network]]> Thu, 18 Jun 2020 16:13:57 GMT /slideshow/how-to-classify-documents-automatically-using-nlp/235854255 skyl_ai@slideshare.net(skyl_ai) How to classify documents automatically using NLP skyl_ai About the webinar Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business. Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts. In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc. What you will learn - How businesses are leveraging document classification to their advantage - Best practice to automate machine learning models in hours not months - Demo: Classify news articles into the right category using convolution neural network <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtoclassifydocumentsautomaticallyusingnlp-200618161357-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business. Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts. In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc. What you will learn - How businesses are leveraging document classification to their advantage - Best practice to automate machine learning models in hours not months - Demo: Classify news articles into the right category using convolution neural network

How to classify documents automatically using NLP from Skyl.ai

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0 AI in Quality Control: How to do visual inspection with AI /slideshow/ai-in-quality-control-how-to-do-visual-inspection-with-ai/235419160 aiinqualitycontrolhowtodovisualinspectionwithai-200611155510
About the webinar Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc. are employing AI-powered solutions to detect defects early and avoid the defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label and train the AI model to detect defects, all within a few minutes. - Address the challenges of AI & Machine learning and how to overcome them.]]>
About the webinar Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc. are employing AI-powered solutions to detect defects early and avoid the defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label and train the AI model to detect defects, all within a few minutes. - Address the challenges of AI & Machine learning and how to overcome them.]]> Thu, 11 Jun 2020 15:55:10 GMT /slideshow/ai-in-quality-control-how-to-do-visual-inspection-with-ai/235419160 skyl_ai@slideshare.net(skyl_ai) AI in Quality Control: How to do visual inspection with AI skyl_ai About the webinar Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc. are employing AI-powered solutions to detect defects early and avoid the defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label and train the AI model to detect defects, all within a few minutes. - Address the challenges of AI & Machine learning and how to overcome them. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiinqualitycontrolhowtodovisualinspectionwithai-200611155510-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc. are employing AI-powered solutions to detect defects early and avoid the defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label and train the AI model to detect defects, all within a few minutes. - Address the challenges of AI & Machine learning and how to overcome them.

AI in Quality Control: How to do visual inspection with AI from Skyl.ai

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0 How to analyze text data for AI and ML with Named Entity Recognition /slideshow/how-to-analyze-text-data-for-ai-and-ml-with-named-entity-recognition/234996187 howtoanalyzetextdataforaiandmlwithnamedentityrecognition-200604155606
About the webinar The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data What you will learn - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months]]>
About the webinar The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data What you will learn - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months]]> Thu, 04 Jun 2020 15:56:06 GMT /slideshow/how-to-analyze-text-data-for-ai-and-ml-with-named-entity-recognition/234996187 skyl_ai@slideshare.net(skyl_ai) How to analyze text data for AI and ML with Named Entity Recognition skyl_ai About the webinar The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data What you will learn - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtoanalyzetextdataforaiandmlwithnamedentityrecognition-200604155606-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc. Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge. Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data What you will learn - How organizations are leveraging Named Entity Recognition across various industries - Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization) - Best practice to automate machine learning models in hours not months

How to analyze text data for AI and ML with Named Entity Recognition from Skyl.ai

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0 AI for Customer Service: How to Improve Contact Center Efficiency with Machine Learning /slideshow/ai-for-customer-service-how-to-improve-contact-center-efficiency-with-machine-learning-234690961/234690961 aiforcustomerservicehowtoimprovecontactcenterefficiencywithmachinelearning-200528180247
About the webinar It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity. Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value. What you will learn - How organizations are building engaging interactions that deliver value to customers - Best practices to automate AI/ML models - Demo: How to route customer queries to the right department or professional]]>
About the webinar It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity. Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value. What you will learn - How organizations are building engaging interactions that deliver value to customers - Best practices to automate AI/ML models - Demo: How to route customer queries to the right department or professional]]> Thu, 28 May 2020 18:02:46 GMT /slideshow/ai-for-customer-service-how-to-improve-contact-center-efficiency-with-machine-learning-234690961/234690961 skyl_ai@slideshare.net(skyl_ai) AI for Customer Service: How to Improve Contact Center Efficiency with Machine Learning skyl_ai About the webinar It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity. Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value. What you will learn - How organizations are building engaging interactions that deliver value to customers - Best practices to automate AI/ML models - Demo: How to route customer queries to the right department or professional <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiforcustomerservicehowtoimprovecontactcenterefficiencywithmachinelearning-200528180247-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity. Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value. What you will learn - How organizations are building engaging interactions that deliver value to customers - Best practices to automate AI/ML models - Demo: How to route customer queries to the right department or professional

AI for Customer Service: How to Improve Contact Center Efficiency with Machine Learning from Skyl.ai

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0 How an AI-backed recommendation system can help increase revenue for your online store /slideshow/how-an-aibacked-recommendation-system-can-help-increase-revenue-for-your-online-store-234425845/234425845 howanai-backedrecommendationsystemcanhelpincreaserevenueforyouronlinestore-200521165748
About the webinar Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick. Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers. This webinar will showcase a live demo of how to build such a robust recommendation model in hours. What you will learn - How e-commerce companies drive sales through AI-powered product recommendation engines - Challenges faced in ML automation and how to overcome those using a unified ML platform - Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform]]>
About the webinar Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick. Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers. This webinar will showcase a live demo of how to build such a robust recommendation model in hours. What you will learn - How e-commerce companies drive sales through AI-powered product recommendation engines - Challenges faced in ML automation and how to overcome those using a unified ML platform - Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform]]> Thu, 21 May 2020 16:57:48 GMT /slideshow/how-an-aibacked-recommendation-system-can-help-increase-revenue-for-your-online-store-234425845/234425845 skyl_ai@slideshare.net(skyl_ai) How an AI-backed recommendation system can help increase revenue for your online store skyl_ai About the webinar Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick. Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers. This webinar will showcase a live demo of how to build such a robust recommendation model in hours. What you will learn - How e-commerce companies drive sales through AI-powered product recommendation engines - Challenges faced in ML automation and how to overcome those using a unified ML platform - Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howanai-backedrecommendationsystemcanhelpincreaserevenueforyouronlinestore-200521165748-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick. Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers. This webinar will showcase a live demo of how to build such a robust recommendation model in hours. What you will learn - How e-commerce companies drive sales through AI-powered product recommendation engines - Challenges faced in ML automation and how to overcome those using a unified ML platform - Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform

How an AI-backed recommendation system can help increase revenue for your online store from Skyl.ai

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0 Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning? /slideshow/future-of-ecommerce-how-to-improve-the-online-shopping-experience-using-machine-learning/233950636 futureofecommercehowtoimproveonlineshoppingexperienceusingmachinelearning-200514160229
About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you will learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai]]>
About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you will learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai]]> Thu, 14 May 2020 16:02:29 GMT /slideshow/future-of-ecommerce-how-to-improve-the-online-shopping-experience-using-machine-learning/233950636 skyl_ai@slideshare.net(skyl_ai) Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning? skyl_ai About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you will learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/futureofecommercehowtoimproveonlineshoppingexperienceusingmachinelearning-200514160229-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you will learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai

Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning? from Skyl.ai

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0 test - Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning? /skyl_ai/test-future-of-ecommerce-how-to-improve-the-online-shopping-experience-using-machine-learning futureofecommercehowtoimproveonlineshoppingexperienceusingmachinelearning-200513144937
About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you'll learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai]]>
About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you'll learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai]]> Wed, 13 May 2020 14:49:37 GMT /skyl_ai/test-future-of-ecommerce-how-to-improve-the-online-shopping-experience-using-machine-learning skyl_ai@slideshare.net(skyl_ai) test - Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning? skyl_ai About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you'll learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/futureofecommercehowtoimproveonlineshoppingexperienceusingmachinelearning-200513144937-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you'll learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai

test - Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning? from Skyl.ai

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0 AI in Healthcare: How to Implement Medical Imaging Using Machine Learning? /slideshow/how-ai-is-changing-medical-imaging-in-the-healthcare-industry-233358121/233358121 howaiischangingmedicalimaginginthehealthcareindustry-200507155743
About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions. Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you will learn: - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai]]>
About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions. Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you will learn: - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai]]> Thu, 07 May 2020 15:57:43 GMT /slideshow/how-ai-is-changing-medical-imaging-in-the-healthcare-industry-233358121/233358121 skyl_ai@slideshare.net(skyl_ai) AI in Healthcare: How to Implement Medical Imaging Using Machine Learning? skyl_ai About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions. Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you will learn: - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howaiischangingmedicalimaginginthehealthcareindustry-200507155743-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions. Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you will learn: - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai

AI in Healthcare: How to Implement Medical Imaging Using Machine Learning? from Skyl.ai

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0 AI in Healthcare: Can AI Help in Diagnosing Coronavirus /slideshow/ai-in-healthcare-can-ai-help-in-diagnosing-coronavirus/232523494 aiinhealthcarecanaihelpindiagnosingcoronavirus-200423164432
About the webinar The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months! According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?' The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor. Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly. What you'll learn - How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected. - Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months. - Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai.]]>
About the webinar The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months! According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?' The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor. Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly. What you'll learn - How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected. - Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months. - Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai.]]> Thu, 23 Apr 2020 16:44:32 GMT /slideshow/ai-in-healthcare-can-ai-help-in-diagnosing-coronavirus/232523494 skyl_ai@slideshare.net(skyl_ai) AI in Healthcare: Can AI Help in Diagnosing Coronavirus skyl_ai About the webinar The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months! According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?' The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor. Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly. What you'll learn - How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected. - Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months. - Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiinhealthcarecanaihelpindiagnosingcoronavirus-200423164432-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months! According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?' The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor. Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly. What you'll learn - How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected. - Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months. - Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai.

AI in Healthcare: Can AI Help in Diagnosing Coronavirus from Skyl.ai

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0 AI in Insurance: How to Automate Insurance Claim Processing with Machine Learning /skyl_ai/ai-in-insurance-how-to-automate-insurance-claim-processing-with-machine-learning-230918469 aiininsurancehowtoautomateinsuranceclaimprocessingwithmachinelearningupdated-200326164250
About the webinar Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you'll learn - How Insurance companies are using ML to drive more efficiency and business gain - Best practices to automate machine learning models - Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai]]>
About the webinar Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you'll learn - How Insurance companies are using ML to drive more efficiency and business gain - Best practices to automate machine learning models - Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai]]> Thu, 26 Mar 2020 16:42:50 GMT /skyl_ai/ai-in-insurance-how-to-automate-insurance-claim-processing-with-machine-learning-230918469 skyl_ai@slideshare.net(skyl_ai) AI in Insurance: How to Automate Insurance Claim Processing with Machine Learning skyl_ai About the webinar Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you'll learn - How Insurance companies are using ML to drive more efficiency and business gain - Best practices to automate machine learning models - Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiininsurancehowtoautomateinsuranceclaimprocessingwithmachinelearningupdated-200326164250-thumbnail.jpg?width=120&height=120&fit=bounds" /> About the webinar Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you'll learn - How Insurance companies are using ML to drive more efficiency and business gain - Best practices to automate machine learning models - Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai

AI in Insurance: How to Automate Insurance Claim Processing with Machine Learning from Skyl.ai

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