際際滷shows by User: VincentdeStoecklin / http://www.slideshare.net/images/logo.gif 際際滷shows by User: VincentdeStoecklin / Mon, 24 Aug 2020 09:54:55 GMT 際際滷Share feed for 際際滷shows by User: VincentdeStoecklin How artificial intelligence (AI) can help maximize customer intelligence ROI /slideshow/how-artificial-intelligence-ai-can-help-maximize-customer-intelligence-roi/238188658 howartificialintelligenceaicanhelpmaximizecustomerintelligenceroi-latestdataiku1-200824095455
The presentation aims to present key challenges and success factors when it comes to deploying high value customer-oriented AI projects. We focus on key use cases (churn, cross-sell, personalization) and present best practices to help build and deploy AI projects, from scoping and data availability to operationalization and adoption. Key takeaways: What are the key AI use cases in Customer Intelligence? How do I prioritize and assess the ROI of my use cases? How can I ensure my AI projects are successful?]]>
The presentation aims to present key challenges and success factors when it comes to deploying high value customer-oriented AI projects. We focus on key use cases (churn, cross-sell, personalization) and present best practices to help build and deploy AI projects, from scoping and data availability to operationalization and adoption. Key takeaways: What are the key AI use cases in Customer Intelligence? How do I prioritize and assess the ROI of my use cases? How can I ensure my AI projects are successful?]]> Mon, 24 Aug 2020 09:54:55 GMT /slideshow/how-artificial-intelligence-ai-can-help-maximize-customer-intelligence-roi/238188658 VincentdeStoecklin@slideshare.net(VincentdeStoecklin) How artificial intelligence (AI) can help maximize customer intelligence ROI VincentdeStoecklin The presentation aims to present key challenges and success factors when it comes to deploying high value customer-oriented AI projects. We focus on key use cases (churn, cross-sell, personalization) and present best practices to help build and deploy AI projects, from scoping and data availability to operationalization and adoption. Key takeaways: What are the key AI use cases in Customer Intelligence? How do I prioritize and assess the ROI of my use cases? How can I ensure my AI projects are successful? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howartificialintelligenceaicanhelpmaximizecustomerintelligenceroi-latestdataiku1-200824095455-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The presentation aims to present key challenges and success factors when it comes to deploying high value customer-oriented AI projects. We focus on key use cases (churn, cross-sell, personalization) and present best practices to help build and deploy AI projects, from scoping and data availability to operationalization and adoption. Key takeaways: What are the key AI use cases in Customer Intelligence? How do I prioritize and assess the ROI of my use cases? How can I ensure my AI projects are successful?
How artificial intelligence (AI) can help maximize customer intelligence ROI from Vincent de Stoecklin
]]> 101 0 https://cdn.slidesharecdn.com/ss_thumbnails/howartificialintelligenceaicanhelpmaximizecustomerintelligenceroi-latestdataiku1-200824095455-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AI projects - Lifecyle & Best Practices /slideshow/ai-projects-lifecyle-best-practices/238188560 aiprojectbestpractices-dataiku-financialservices-200824095030
As companies around the world look to get a jump on AI efforts, theres one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects? Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this presentation we share a comprehensive framework for: 1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis. 2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics. 3) Operationalization of AI projects: challenges and best practices. ]]>
As companies around the world look to get a jump on AI efforts, theres one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects? Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this presentation we share a comprehensive framework for: 1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis. 2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics. 3) Operationalization of AI projects: challenges and best practices. ]]> Mon, 24 Aug 2020 09:50:30 GMT /slideshow/ai-projects-lifecyle-best-practices/238188560 VincentdeStoecklin@slideshare.net(VincentdeStoecklin) AI projects - Lifecyle & Best Practices VincentdeStoecklin As companies around the world look to get a jump on AI efforts, theres one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects? Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this presentation we share a comprehensive framework for: 1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis. 2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics. 3) Operationalization of AI projects: challenges and best practices. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiprojectbestpractices-dataiku-financialservices-200824095030-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> As companies around the world look to get a jump on AI efforts, theres one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects? Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this presentation we share a comprehensive framework for: 1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis. 2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics. 3) Operationalization of AI projects: challenges and best practices.
AI projects - Lifecyle & Best Practices from Vincent de Stoecklin
]]> 74 0 https://cdn.slidesharecdn.com/ss_thumbnails/aiprojectbestpractices-dataiku-financialservices-200824095030-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction la Data Science l data business https://fr.slideshare.net/VincentdeStoecklin/introduction-to-data-science-l-data-business introductiontodatascienceldata-business-150207060859-conversion-gate02
Introduction la Data Science : r辿gression, classification, , clustering, machine learning...vue d'ensemble, quelques techniques et des ressources pour creuser le sujet.]]>
Introduction la Data Science : r辿gression, classification, , clustering, machine learning...vue d'ensemble, quelques techniques et des ressources pour creuser le sujet.]]> Sat, 07 Feb 2015 06:08:59 GMT https://fr.slideshare.net/VincentdeStoecklin/introduction-to-data-science-l-data-business VincentdeStoecklin@slideshare.net(VincentdeStoecklin) Introduction la Data Science l data business VincentdeStoecklin Introduction la Data Science : r辿gression, classification, , clustering, machine learning...vue d'ensemble, quelques techniques et des ressources pour creuser le sujet. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontodatascienceldata-business-150207060859-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Introduction la Data Science : r辿gression, classification, , clustering, machine learning...vue d&#39;ensemble, quelques techniques et des ressources pour creuser le sujet.
from Vincent de Stoecklin
]]> 3620 423 https://cdn.slidesharecdn.com/ss_thumbnails/introductiontodatascienceldata-business-150207060859-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big data - Cours d'introduction l Data-business https://fr.slideshare.net/slideshow/big-data-cours-dintroduction-l-databusiness/41397645 bigdata-coursdintroductionldata-business-141111044129-conversion-gate01
Cours d'introduction au Big Data : d辿fintion, fondamentaux, explication des 3V, exemples d'application, aper巽u des innovations technologiques.]]>
Cours d'introduction au Big Data : d辿fintion, fondamentaux, explication des 3V, exemples d'application, aper巽u des innovations technologiques.]]> Tue, 11 Nov 2014 04:41:28 GMT https://fr.slideshare.net/slideshow/big-data-cours-dintroduction-l-databusiness/41397645 VincentdeStoecklin@slideshare.net(VincentdeStoecklin) Big data - Cours d'introduction l Data-business VincentdeStoecklin Cours d'introduction au Big Data : d辿fintion, fondamentaux, explication des 3V, exemples d'application, aper巽u des innovations technologiques. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdata-coursdintroductionldata-business-141111044129-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Cours d&#39;introduction au Big Data : d辿fintion, fondamentaux, explication des 3V, exemples d&#39;application, aper巽u des innovations technologiques.
from Vincent de Stoecklin
]]> 23786 40 https://cdn.slidesharecdn.com/ss_thumbnails/bigdata-coursdintroductionldata-business-141111044129-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-VincentdeStoecklin-48x48.jpg?cb=1645747540 Passionate about Technology and convinced by the power of Data to improve decision-making. In charge of Partnerships at Dataiku, a french Data Science company focused on making Data Teams more efficient when prototyping, building and deploying data-driven applications. Founder of Data-Business.fr, french community about Big Data, Analytics and Dataviz. Previously consultant in the realm of Digital Transformation. Visiting Teacher in Engineering and Business schools on Big Data and Data Science topics http://www.data-business.fr https://cdn.slidesharecdn.com/ss_thumbnails/howartificialintelligenceaicanhelpmaximizecustomerintelligenceroi-latestdataiku1-200824095455-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-artificial-intelligence-ai-can-help-maximize-customer-intelligence-roi/238188658 How artificial intelli... https://cdn.slidesharecdn.com/ss_thumbnails/aiprojectbestpractices-dataiku-financialservices-200824095030-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ai-projects-lifecyle-best-practices/238188560 AI projects - Lifecyle... https://cdn.slidesharecdn.com/ss_thumbnails/introductiontodatascienceldata-business-150207060859-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds VincentdeStoecklin/introduction-to-data-science-l-data-business Introduction la Data...