Dark Matter, Public Health, and Scientific ComputingGreg Wilson
?
This document discusses the lack of computational skills among many scientists and proposes ways to improve this situation. It notes that most scientists focus on their research and do not engage with new technologies. Short, practical training courses targeting graduate students can help more scientists learn basic skills like shell scripting, version control, and testing. These skills allow scientists to be more productive and make new technologies more accessible, even if the skills are not publishable. The document advocates teaching a bit of computing in every course, hosting workshops, and changing incentives to better support computational reproducibility and open science.
Since 2008, over 100 students from 16 universities have worked in distributed teams on open source projects for course credit. Using Basie (http://basieproject.org) as an example, this talk explains how we have made that work. This talk was given at PyCon 2010 in Atlanta on February 20, 2010.
We Know Less Than You Think (But We Do Know Something)Greg Wilson
?
A quick summary of empirical software engineering, followed by early results from a survey of how over 1900 scientists use computers in their research.
The document summarizes research in software engineering and development practices. It discusses several studies that have provided evidence for practices like rigorous inspections reducing errors, Conway's Law relating organizational structure to system structure, and physical distance not affecting post-release fault rates as much as distance in the organizational chart. The document advocates building development practices around these empirical facts and calls for continued work to systematically synthesize research evidence and practices.
- IT is an adaptive world that aims to fit business needs and create new opportunities.
- Programming requires making things work first before optimizing for speed.
- Software development is an iterative process where the initial solution is rarely the final solution.
Getting to Flow in Software Development (ASWEC 2014 Keynote)Gail Murphy
?
Humans are amazing at processing information. It is a good thing that they are because software development projects generate a tremendous amount of information of various forms from predominantly natural language documents like requirements to blended natural language and structured artifacts like issues to predominantly structured source and test code. For some time, the amount of information produced daily in a large software development has exceeded a human¡¯s ability to process that information. Instead of producing tools that allow a software developer to focus on information pertinent to a task, too many tools have been built that focus solely on producing as much information as possible. In this talk, I will discuss interaction styles for tools that may bring us closer to keeping a developer in the flow of a task. By improving flow, we can enable developers to work smarter, work better and have more fun.
The Snail Entrepreneur: The 7-year-old kid every startup should learn fromClaudio Perrone
?
Matteo faced a seemly impossible problem, but didn't give up. He used daddy's #PopcornFlow and pivoted. 17 options and 5 experiments later, he converged to success.
PopcornFlow is impacting businesses (large and small) but also families and kids.
If you like this story, please contribute to Matteo's cause.
Getting Things Done for Technical CommunicatorsKaren Mardahl
?
A TCUK15 workshop by John Kearney and Karen Mardahl at the ISTC's technical communication conference on September 29th in Glasgow, Scotland. Script for the workshop is at http://www.mardahl.dk/2015/10/29/the-getting-things-done-workshop-at-tcuk15/.
This document discusses research into measuring and improving software development productivity on a minute-by-minute basis. It explores developers' perspectives on productivity, observes their activities and work flows, and suggests ways forward at the individual, team, and organizational levels. Key findings include that development work is highly fragmented, habitual productivity patterns exist, and mitigating context switches can improve focus. The research aims to develop more flexible and adaptable measurement and retrospective tools to help developers and organizations enhance productivity.
Getting Things Done for Technical Communicators at TCUK14Karen Mardahl
?
My presentation at TCUK14 in Brighton in September 2014 - technicalcommunicationuk.com. It is an update of my similar presentation in June at UA Europe.
Presented at Agile Prague (15th September 2014)
Video available at https://vimeo.com/107919080
Apparently, everyone knows about patterns. Except for the ones that don't. Which is basically all the people who've never come across patterns... plus most of the people who have.
Singleton is often treated as a must-know design pattern. Patterns are sometimes considered to be the basis of blueprint-driven architecture. Patterns are also seen as something you don't need to know any more because you've got frameworks, libraries and middleware by the download. Or that patterns are something you don't need to know because you're building on UML, legacy code or emergent design. There are all these misconceptions about patterns... and more.
In this talk, let's take an alternative tour of patterns, one that is based on improving the habitability of code, the expression of process and the habit of practice. Patterns are about communication, exploration, empiricism, reasoning, incremental development, sharing design and bridging rather than barricading different levels of expertise.
Andres Rodriguez at AI Frontiers: Catalyzing Deep Learning's Impact in the En...AI Frontiers
?
Intel Nervana has built a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. Nervana¡¯s platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
Workshop on using the Experience API (xAPI or TinCan) for education research. Presented by Ellen Meiselman, David Topps and Corey Albersworth, at Medbiq Annual Conference, Baltimore, MD in May 2016.
1. Udemy has learned lessons from serving over 190 million lessons to students. Key aspects of maintaining company culture include onboarding new employees effectively, using automation to standardize processes, and enforcing coding standards.
2. It is important to measure business metrics at scale to understand trends rather than individual events. A/B testing and feature flags can provide insights into how changes may impact the business.
3. Questioning assumptions is important, such as deciding to move away from a custom PHP framework to a more standard framework like Django in order to reduce technical debt and speed up developer onboarding. Major changes like this require serious commitment over multiple years.
4. The core mission of helping anyone learn anything through
This document discusses psychology and engineering in testing. It describes the structure of European product development teams and how testers work embedded in agile teams. It advocates for automating some tests but also focusing on social aspects of testing. The document outlines how the authors' company transitioned from having testers separate from developers to integrated into teams. It provides their agile testing manifesto and emphasizes collaboration, learning, and using a combination of automated and manual testing. Finally, it provides advice for how others can implement a similar approach and encourages contacting the authors for hiring or sharing experiences.
What is Python? An overview of Python for science.Nicholas Pringle
?
Python is a general purpose, high-level, free and open-source programming language that is readable and intuitive. It has strong scientific computing packages like NumPy, SciPy, and Matplotlib that allow it to be used for tasks like MATLAB. Python emphasizes code readability and reusability through standards like PEP8 and version control, making it well-suited for collaboration between individual, institutional, and developer users in its large, diverse community.
The genesis of clusterlib - An open source library to tame your favourite sup...Arnaud Joly
?
The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects.
The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE.
10 more lessons learned from building Machine Learning systems - MLConfXavier Amatriain
?
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. It is important to focus on feature engineering to create features that are reusable, transformable, interpretable, and reliable. The outputs of models may become inputs to other models, so care must be taken to avoid feedback loops and ensure proper data dependencies.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
?
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
?
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
An overview of Tensorflow, and then we'll walk through how to utilize this library within the H2O platform. Tensorflow is an open source, deep learning framework utilized by Google and Deepmind. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
More information, visit: http://www.godatadriven.com/accelerator.html
Data scientists aren¡¯t a nice-to-have anymore, they are a must-have. Businesses of all sizes are scooping up this new breed of engineering professional. But how do you find the right one for your business?
The Data Science Accelerator Program is a one year program, delivered in Amsterdam by world-class industry practitioners. It provides your aspiring data scientists with intensive on- and off-site instruction, access to an extensive network of speakers and mentors and coaching.
The Data Science Accelerator Program helps you assess and radically develop the skills of your data science staff or recruits.
Our goal is to deliver you excellent data scientists that help you become a data driven enterprise.
The right tools
We teach your organisation the proven data science tools.
The right hands
We are trusted by many industry leading partners.
The right experience
We've done big data and data science at many clients, we know what the real world is like.
The right experts
We have a world class selection of lecturers that you will be working with.
Vincent D. Warmerdam
Jonathan Samoocha
Ivo Everts
Rogier van der Geer
Ron van Weverwijk
Giovanni Lanzani
The right curriculum
We meet twice a month. Once for a lecture, once for a hackathon.
Lectures
The RStudio stack.
The art of simulation.
The iPython stack.
Linear modelling.
Operations research.
Nonlinear modelling.
Clustering & ensemble methods.
Natural language processing.
Time series.
Visualisation.
Scaling to big data.
Advanced topics.
Hackathons
Scrape and mine the internet.
Solving multiarmed bandit problems.
Webdev with flask and pandas as a backend.
Build an automation script for linear models.
Build a heuristic tsp solver.
Code review your automation for nonlinear models.
Build a method that outperforms random forests.
Build a markov chain to generate song lyrics.
Predict an optimal portfolio for the stock market.
Create an interactive d3 app with backend.
Start up a spark cluster with large s3 data.
You pick!
Interested?
Ping us here. signal@godatadriven.com
Workshop slides which give an overview of python programming. The slides are accompanied by DIY (do it yourself) programs which can be found as in GitHub (https://github.com/bhalajin/blueprints)
The Journey of Large Language Models at GetYourGuideZilliz
?
"Integrating Large Language Models (LLMs) into our workflows at GetYourGuide has been quite the adventure. In this talk, I¡¯ll share our experience with LLMs, focusing on the products we¡¯ve built , the challenges we faced, and the impact on our business.
We¡¯ll explore the exciting use cases, technical hurdles like integration and scaling, as well as our architectural decisions. Additionally, I¡¯ll discuss our approach to dealing with hallucinations, a common downside of LLMs.
By sharing real examples from GetYourGuide, I¡¯ll highlight what worked well and what didn¡¯t, offering a handy guide for other organisations looking to tap into the power of LLMs."
An introductory talk on scientific computing in Python. Statistics, probability and linear algebra, are important aspects of computing/computer modeling and the same is covered here.
Many companies are looking for "DevOps'' in many forms, but what kind of skills or experiences are actually needed? I¡¯ll debunk some of the myths surrounding what recruiters or internet lurkers might tell you and find out if you might actually have an aptitude for Site Reliability or Infrastructure Engineering. If so, what might be good knowledge areas to get started with? And if learning leads to an interview, what might that look like?
This document provides an overview of using TensorFlow and Quarkus to build intelligent applications that serve machine learning models. It begins with an introduction and agenda. It then discusses TensorFlow and how it can be used to build and train machine learning models. It demonstrates how a TensorFlow model can be served using Quarkus and consumed via HTTP requests. The technical benefits of serving models with Quarkus are described. Finally, use cases, additional resources, and a Q&A section are outlined.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
This document discusses research into measuring and improving software development productivity on a minute-by-minute basis. It explores developers' perspectives on productivity, observes their activities and work flows, and suggests ways forward at the individual, team, and organizational levels. Key findings include that development work is highly fragmented, habitual productivity patterns exist, and mitigating context switches can improve focus. The research aims to develop more flexible and adaptable measurement and retrospective tools to help developers and organizations enhance productivity.
Getting Things Done for Technical Communicators at TCUK14Karen Mardahl
?
My presentation at TCUK14 in Brighton in September 2014 - technicalcommunicationuk.com. It is an update of my similar presentation in June at UA Europe.
Presented at Agile Prague (15th September 2014)
Video available at https://vimeo.com/107919080
Apparently, everyone knows about patterns. Except for the ones that don't. Which is basically all the people who've never come across patterns... plus most of the people who have.
Singleton is often treated as a must-know design pattern. Patterns are sometimes considered to be the basis of blueprint-driven architecture. Patterns are also seen as something you don't need to know any more because you've got frameworks, libraries and middleware by the download. Or that patterns are something you don't need to know because you're building on UML, legacy code or emergent design. There are all these misconceptions about patterns... and more.
In this talk, let's take an alternative tour of patterns, one that is based on improving the habitability of code, the expression of process and the habit of practice. Patterns are about communication, exploration, empiricism, reasoning, incremental development, sharing design and bridging rather than barricading different levels of expertise.
Andres Rodriguez at AI Frontiers: Catalyzing Deep Learning's Impact in the En...AI Frontiers
?
Intel Nervana has built a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. Nervana¡¯s platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
Workshop on using the Experience API (xAPI or TinCan) for education research. Presented by Ellen Meiselman, David Topps and Corey Albersworth, at Medbiq Annual Conference, Baltimore, MD in May 2016.
1. Udemy has learned lessons from serving over 190 million lessons to students. Key aspects of maintaining company culture include onboarding new employees effectively, using automation to standardize processes, and enforcing coding standards.
2. It is important to measure business metrics at scale to understand trends rather than individual events. A/B testing and feature flags can provide insights into how changes may impact the business.
3. Questioning assumptions is important, such as deciding to move away from a custom PHP framework to a more standard framework like Django in order to reduce technical debt and speed up developer onboarding. Major changes like this require serious commitment over multiple years.
4. The core mission of helping anyone learn anything through
This document discusses psychology and engineering in testing. It describes the structure of European product development teams and how testers work embedded in agile teams. It advocates for automating some tests but also focusing on social aspects of testing. The document outlines how the authors' company transitioned from having testers separate from developers to integrated into teams. It provides their agile testing manifesto and emphasizes collaboration, learning, and using a combination of automated and manual testing. Finally, it provides advice for how others can implement a similar approach and encourages contacting the authors for hiring or sharing experiences.
What is Python? An overview of Python for science.Nicholas Pringle
?
Python is a general purpose, high-level, free and open-source programming language that is readable and intuitive. It has strong scientific computing packages like NumPy, SciPy, and Matplotlib that allow it to be used for tasks like MATLAB. Python emphasizes code readability and reusability through standards like PEP8 and version control, making it well-suited for collaboration between individual, institutional, and developer users in its large, diverse community.
The genesis of clusterlib - An open source library to tame your favourite sup...Arnaud Joly
?
The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects.
The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE.
10 more lessons learned from building Machine Learning systems - MLConfXavier Amatriain
?
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. It is important to focus on feature engineering to create features that are reusable, transformable, interpretable, and reliable. The outputs of models may become inputs to other models, so care must be taken to avoid feedback loops and ensure proper data dependencies.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
?
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
?
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
An overview of Tensorflow, and then we'll walk through how to utilize this library within the H2O platform. Tensorflow is an open source, deep learning framework utilized by Google and Deepmind. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
More information, visit: http://www.godatadriven.com/accelerator.html
Data scientists aren¡¯t a nice-to-have anymore, they are a must-have. Businesses of all sizes are scooping up this new breed of engineering professional. But how do you find the right one for your business?
The Data Science Accelerator Program is a one year program, delivered in Amsterdam by world-class industry practitioners. It provides your aspiring data scientists with intensive on- and off-site instruction, access to an extensive network of speakers and mentors and coaching.
The Data Science Accelerator Program helps you assess and radically develop the skills of your data science staff or recruits.
Our goal is to deliver you excellent data scientists that help you become a data driven enterprise.
The right tools
We teach your organisation the proven data science tools.
The right hands
We are trusted by many industry leading partners.
The right experience
We've done big data and data science at many clients, we know what the real world is like.
The right experts
We have a world class selection of lecturers that you will be working with.
Vincent D. Warmerdam
Jonathan Samoocha
Ivo Everts
Rogier van der Geer
Ron van Weverwijk
Giovanni Lanzani
The right curriculum
We meet twice a month. Once for a lecture, once for a hackathon.
Lectures
The RStudio stack.
The art of simulation.
The iPython stack.
Linear modelling.
Operations research.
Nonlinear modelling.
Clustering & ensemble methods.
Natural language processing.
Time series.
Visualisation.
Scaling to big data.
Advanced topics.
Hackathons
Scrape and mine the internet.
Solving multiarmed bandit problems.
Webdev with flask and pandas as a backend.
Build an automation script for linear models.
Build a heuristic tsp solver.
Code review your automation for nonlinear models.
Build a method that outperforms random forests.
Build a markov chain to generate song lyrics.
Predict an optimal portfolio for the stock market.
Create an interactive d3 app with backend.
Start up a spark cluster with large s3 data.
You pick!
Interested?
Ping us here. signal@godatadriven.com
Workshop slides which give an overview of python programming. The slides are accompanied by DIY (do it yourself) programs which can be found as in GitHub (https://github.com/bhalajin/blueprints)
The Journey of Large Language Models at GetYourGuideZilliz
?
"Integrating Large Language Models (LLMs) into our workflows at GetYourGuide has been quite the adventure. In this talk, I¡¯ll share our experience with LLMs, focusing on the products we¡¯ve built , the challenges we faced, and the impact on our business.
We¡¯ll explore the exciting use cases, technical hurdles like integration and scaling, as well as our architectural decisions. Additionally, I¡¯ll discuss our approach to dealing with hallucinations, a common downside of LLMs.
By sharing real examples from GetYourGuide, I¡¯ll highlight what worked well and what didn¡¯t, offering a handy guide for other organisations looking to tap into the power of LLMs."
An introductory talk on scientific computing in Python. Statistics, probability and linear algebra, are important aspects of computing/computer modeling and the same is covered here.
Many companies are looking for "DevOps'' in many forms, but what kind of skills or experiences are actually needed? I¡¯ll debunk some of the myths surrounding what recruiters or internet lurkers might tell you and find out if you might actually have an aptitude for Site Reliability or Infrastructure Engineering. If so, what might be good knowledge areas to get started with? And if learning leads to an interview, what might that look like?
This document provides an overview of using TensorFlow and Quarkus to build intelligent applications that serve machine learning models. It begins with an introduction and agenda. It then discusses TensorFlow and how it can be used to build and train machine learning models. It demonstrates how a TensorFlow model can be served using Quarkus and consumed via HTTP requests. The technical benefits of serving models with Quarkus are described. Finally, use cases, additional resources, and a Q&A section are outlined.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
building intelligent systems with large scale deep learningmustafa sarac
?
The document discusses the work of the Google Brain team in conducting long-term research on machine learning and building systems like TensorFlow to make ML models more widely available. It outlines the team's goals of making machines intelligent to improve people's lives through research areas like computer vision, healthcare, robotics and language understanding. The team aims to build general tools for ML and collaborate within Google and with others to apply their research at large scale.
How to build and run a big data platform in the 21st centuryAli Dasdan
?
The document provides an overview of big data platform architectures that have been built by various companies and organizations. It discusses self-built platforms from companies like Airbnb, Netflix, Facebook, Slack, and Uber. It also covers cloud-built platforms on IBM Cloud, Microsoft Azure, Google Cloud, and Amazon AWS. Consulting-built platforms from Cloudera and ThoughtWorks are presented. Finally, it introduces the NIST Big Data Reference Architecture as a standard reference model and discusses generic batch vs streaming architectures like Lambda and Kappa.
This document provides an overview of machine learning and perspectives from various experts:
- It discusses different types of machine learning problems like classification, regression, and clustering and examples of algorithms used to solve each.
- Experts offer views on neural networks, with one saying they are like a "swiss army knife" and can be used to solve many machine learning problems.
- Other experts discuss the importance of linear algebra and matrix multiplication in machine learning models like neural networks.
- One expert prefers neural networks and singular value decomposition for machine learning tasks.
Aws uk ug #8 not everything that happens in vegas stay in vegasPeter Mounce
?
This document discusses various topics related to DevOps practices at different companies:
1. Netflix prioritizes speed of innovation and availability over running costs when developing software. They found this approach ended up costing less than expected.
2. Riot Games uses tools like Chef to deploy their massively multiplayer online game League of Legends to the cloud. This helps them solve launch issues and scale efficiently.
3. Many companies like Netflix, Riot Games, and Kickstarter test new code and configurations in production at a large scale to continuously improve their systems and user experience.
4. Centralized logging services are important for developers to more easily monitor systems, debug issues, and reduce time spent on call
- The document is a programming tutorial that introduces Python and MATLAB for programming in medical imaging.
- It discusses what a computer program is, explains programming languages and code, and why learning to program is useful.
- The tutorial compares Python and MATLAB, noting that both can be used for the course but examples will focus on MATLAB. It outlines differences like MATLAB requiring a license while Python is free.
Kusto (Azure Data Explorer) Training for R&D - January 2019 Tal Bar-Zvi
?
This document summarizes a training presentation on Azure Data Explorer (Kusto). The presentation covered:
1. An introduction to Kusto as a new way to analyze big data and logs that is fast, easy to use, and helps understand services quickly.
2. Examples of different Kusto query types including counting, filtering, aggregating, rendering graphs, and combining queries.
3. How Kusto is used at Taboola to analyze HTTP logs from their CDN, including database sizes and architecture.
4. Additional features like dashboards, alerts, notebooks, and community resources for learning more.
5. A question and answer session addressing common questions about Kusto.
Backstage Software Templates for Java DevelopersMarkus Eisele
?
As a Java developer you might have a hard time accepting the limitations that you feel being introduced into your development cycles. Let's look at the positives and learn everything important to know to turn Backstag's software templates into a helpful tool you can use to elevate the platform experience for all developers.
TrustArc Webinar - Building your DPIA/PIA Program: Best Practices & TipsTrustArc
?
Understanding DPIA/PIAs and how to implement them can be the key to embedding privacy in the heart of your organization as well as achieving compliance with multiple data protection / privacy laws, such as GDPR and CCPA. Indeed, the GDPR mandates Privacy by Design and requires documented Data Protection Impact Assessments (DPIAs) for high risk processing and the EU AI Act requires an assessment of fundamental rights.
How can you build this into a sustainable program across your business? What are the similarities and differences between PIAs and DPIAs? What are the best practices for integrating PIAs/DPIAs into your data privacy processes?
Whether you're refining your compliance framework or looking to enhance your PIA/DPIA execution, this session will provide actionable insights and strategies to ensure your organization meets the highest standards of data protection.
Join our panel of privacy experts as we explore:
- DPIA & PIA best practices
- Key regulatory requirements for conducting PIAs and DPIAs
- How to identify and mitigate data privacy risks through comprehensive assessments
- Strategies for ensuring documentation and compliance are robust and defensible
- Real-world case studies that highlight common pitfalls and practical solutions
Understanding Traditional AI with Custom Vision & MuleSoft.pptxshyamraj55
?
Understanding Traditional AI with Custom Vision & MuleSoft.pptx | ### ºÝºÝߣ Deck Description:
This presentation features Atul, a Senior Solution Architect at NTT DATA, sharing his journey into traditional AI using Azure's Custom Vision tool. He discusses how AI mimics human thinking and reasoning, differentiates between predictive and generative AI, and demonstrates a real-world use case. The session covers the step-by-step process of creating and training an AI model for image classification and object detection¡ªspecifically, an ad display that adapts based on the viewer's gender. Atulavan highlights the ease of implementation without deep software or programming expertise. The presentation concludes with a Q&A session addressing technical and privacy concerns.
Inside Freshworks' Migration from Cassandra to ScyllaDB by Premkumar PatturajScyllaDB
?
Freshworks migrated from Cassandra to ScyllaDB to handle growing audit log data efficiently. Cassandra required frequent scaling, complex repairs, and had non-linear scaling. ScyllaDB reduced costs with fewer machines and improved operations. Using Zero Downtime Migration (ZDM), they bulk-migrated data, performed dual writes, and validated consistency.
Computational Photography: How Technology is Changing Way We Capture the WorldHusseinMalikMammadli
?
? Computational Photography (Computer Vision/Image): How Technology is Changing the Way We Capture the World
He? d¨¹?¨¹nm¨¹s¨¹n¨¹zm¨¹, m¨¹asir smartfonlar v? kameralar nec? bu q?d?r g?z?l g?r¨¹nt¨¹l?r yarad?r? Bunun sirri Computational Fotoqrafiyas?nda(Computer Vision/Imaging) gizlidir¡ª??kill?ri ??km? v? emal etm? ¨¹sulumuzu t?kmill??dir?n, komp¨¹ter elmi il? fotoqrafiyan?n inqilabi birl??m?si.
https://ncracked.com/7961-2/
Note: >> Please copy the link and paste it into Google New Tab now Download link
Free Download Wondershare Filmora 14.3.2.11147 Full Version - All-in-one home video editor to make a great video.Free Download Wondershare Filmora for Windows PC is an all-in-one home video editor with powerful functionality and a fully stacked feature set. Filmora has a simple drag-and-drop top interface, allowing you to be artistic with the story you want to create.Video Editing Simplified - Ignite Your Story. A powerful and intuitive video editing experience. Filmora 10 hash two new ways to edit: Action Cam Tool (Correct lens distortion, Clean up your audio, New speed controls) and Instant Cutter (Trim or merge clips quickly, Instant export).Filmora allows you to create projects in 4:3 or 16:9, so you can crop the videos or resize them to fit the size you want. This way, quickly converting a widescreen material to SD format is possible.
https://ncracked.com/7961-2/
Note: >> Please copy the link and paste it into Google New Tab now Download link
Brave is a free Chromium browser developed for Win Downloads, macOS and Linux systems that allows users to browse the internet in a safer, faster and more secure way than its competition. Designed with security in mind, Brave automatically blocks ads and trackers which also makes it faster,
As Brave naturally blocks unwanted content from appearing in your browser, it prevents these trackers and pop-ups from slowing Download your user experience. It's also designed in a way that strips Downloaden which data is being loaded each time you use it. Without these components
DealBook of Ukraine: 2025 edition | AVentures CapitalYevgen Sysoyev
?
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2024 and the first deals of 2025.
Future-Proof Your Career with AI OptionsDianaGray10
?
Learn about the difference between automation, AI and agentic and ways you can harness these to further your career. In this session you will learn:
Introduction to automation, AI, agentic
Trends in the marketplace
Take advantage of UiPath training and certification
In demand skills needed to strategically position yourself to stay ahead
? If you have any questions or feedback, please refer to the "Women in Automation 2025" dedicated Forum thread. You can find there extra details and updates.
30B Images and Counting: Scaling Canva's Content-Understanding Pipelines by K...ScyllaDB
?
Scaling content understanding for billions of images is no easy feat. This talk dives into building extreme label classification models, balancing accuracy & speed, and optimizing ML pipelines for scale. You'll learn new ways to tackle real-time performance challenges in massive data environments.
DevNexus - Building 10x Development Organizations.pdfJustin Reock
?
Developer Experience is Dead! Long Live Developer Experience!
In this keynote-style session, we¡¯ll take a detailed, granular look at the barriers to productivity developers face today and modern approaches for removing them. 10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ¡®The Coding War Games.¡¯
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method, we invent to deliver products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches works? DORA? SPACE? DevEx? What should we invest in and create urgency behind today so we don¡¯t have the same discussion again in a decade?
Formal Methods: Whence and Whither? [Martin Fr?nzle Festkolloquium, 2025]Jonathan Bowen
?
Alan Turing arguably wrote the first paper on formal methods 75 years ago. Since then, there have been claims and counterclaims about formal methods. Tool development has been slow but aided by Moore¡¯s Law with the increasing power of computers. Although formal methods are not widespread in practical usage at a heavyweight level, their influence as crept into software engineering practice to the extent that they are no longer necessarily called formal methods in their use. In addition, in areas where safety and security are important, with the increasing use of computers in such applications, formal methods are a viable way to improve the reliability of such software-based systems. Their use in hardware where a mistake can be very costly is also important. This talk explores the journey of formal methods to the present day and speculates on future directions.
UiPath Agentic Automation Capabilities and OpportunitiesDianaGray10
?
Learn what UiPath Agentic Automation capabilities are and how you can empower your agents with dynamic decision making. In this session we will cover these topics:
What do we mean by Agents
Components of Agents
Agentic Automation capabilities
What Agentic automation delivers and AI Tools
Identifying Agent opportunities
? If you have any questions or feedback, please refer to the "Women in Automation 2025" dedicated Forum thread. You can find there extra details and updates.
What Makes "Deep Research"? A Dive into AI AgentsZilliz
?
About this webinar:
Unless you live under a rock, you will have heard about OpenAI¡¯s release of Deep Research on Feb 2, 2025. This new product promises to revolutionize how we answer questions requiring the synthesis of large amounts of diverse information. But how does this technology work, and why is Deep Research a noticeable improvement over previous attempts? In this webinar, we will examine the concepts underpinning modern agents using our basic clone, Deep Searcher, as an example.
Topics covered:
Tool use
Structured output
Reflection
Reasoning models
Planning
Types of agentic memory
What Makes "Deep Research"? A Dive into AI AgentsZilliz
?
How to Become Rich, Famous, and Popular While Using Your Programming Skills to Make the World a Better Place
1. How to Become
Rich, Famous, and
Popular While Using
Your Programming
Skills to Make the
World a Better
Place
(Maybe)
Greg Wilson
2. 2
5-15% use GPU clusters
to analyze petabytes
in the cloud
85-95% send each
other spreadsheets
by email
Scientists
3. 3
It Is Therefore Obvious That...
We should put more computing in the curriculum!
But it's already full
4. 4
It Is Therefore Obvious That...
And even if we did...
...who would teach those classes?
5. 5
If you build a man a fire,
you'll keep him warm for a night.
If you set a man on fire,
you'll keep him warm for the rest of his life.
¡ª Terry Pratchett
7. 7
What We Actually Teach
Unix shell => Task automation
Version control => Track and share work
Python/R/MATLAB => Modular programming
SQL => Data management
Make => Reproducibility
8. 8
How We Teach
¡ñ Peer instructors
¡ñ Teach in pairs
¡ñ Learners use their own machines
¡ñ Live coding
¡ñ Sticky notes
¡ñ Collaborative note-taking
¡ñ Iterate, iterate, iterate...
14. 14
What We've Accomplished
¡ñ Honestly don't know
¡ñ Anecdotally, save people 10-20% of
their time for the rest of their careers
¡ñ And prepare them for petabytes and
clouds
15. 15
What I've Learned
1. We know a lot
about learning
2. Most people
would rather fail
than change
3. There are lots of
gaps to fill
17. 17
¡ñ
Today's MOOC platforms don't support PI
¡ñ
Piotr Banaszkiewicz showed it's possible
github.com/pbanaszkiewicz/peer-instruction
¡ñ
Opportunity #1
23. 23
Generalizes Well
First-class support for diffing and
merging spreadsheets will get more
scientists to use version control than
anything else we can do.
3 developers x 8 months (?)
(even if it's optimistic...)
24. 24
Speaking of Version Control
¡ñ Git is an awful tool
¡ñ git-man-page-generator.lokaltog.net
¡ñ But it's the price people have to pay in
order to use GitHub
25. 25
Speaking of Version Control
¡ñ A rational re-design of Git is possible
and worthwhile
¡ñ Opportunity #3
¡ñ Also generalizes well
Andreas Stefik and Susanna Siebert: "An Empirical
Investigation into Programming Language Syntax."
ACM Transactions on Computing Education, 13(4), Nov.
2013.
26. 26
A Puzzle
¡ñ Thousands contribute
patches to open source
software projects
¡ñ Millions have edited
Wikipedia
¡ñ Why don't people build
lessons this way?
27. 27
All Together Now
¡ñ We've shown it can be done
¡ñ And that it's useful
¡ñ This presents more opportunities
28. 28
A Small Part of the Reason
¡ñ ºÝºÝߣshow formats
aren't diffable either
¡ñ But HTML alternatives
are impoverished
29. 29
A Small Part of the Reason
¡ñ Neither can create web-
native videos
¡ñ Proof: pause a video, highlight the
text being displayed, and copy it
32. 32
To Sum Up
¡ñ First-class support for peer instruction
¡ñ Diff for all! (Excel first)
¡ñ A rational reconstruction of Git
¡ñ Browsercast
¡ñ Rocket science
¡ñ Fill in the gaps