I contributed to an article titled “Our 5 Favorite ML/AI Talks from Build 2020” in the June 2020 edition of the online PureAI Web site. See https://pureai.com/articles/2020/06/04/build-recap.aspx.

Microsoft Build is an annual conference aimed at software engineers and web developers who use Microsoft and open source technologies. The 2020 edition of Build was held from Tuesday May 19 through Thursday May 21. The event was completely online and free rather than in-person (and expensive) due to the COVID-19 pandemic.

The 2020 edition of Build featured approximately 40 ML/AI sessions. I watched all of the ML/AI recorded sessions and picked out five of my favorites.

All of the 2020 Build presentations, including the ones that are described in the article, can be accessed at https://mybuild.microsoft.com.

1. “Train and Deploy ML Models at Scale Using Azure Machine Learning” by Chris Lauren & Sabina Cartacio. This 30-minute presentation begins with Chris giving an excellent overview of three of the most important parts of Azure Machine Learning: 1.) creating an ML model completely automatically using the AutoML command line system, 2.) creating an ML model using the GUI-based Azure Machine Learning Studio and 3.) creating a customized ML model using Jupyter notebooks on an Azure virtual machine.

2. “The Future of Tech, With Kevin Scott and Guests” by Kevin Scott and Peter Lee. This keynote presentation covers a lot of ground and is mostly inspirational as opposed to a nuts-and-bolts how-to type of presentation. Kevin Scott is an executive vice president who was recently placed in charge of Microsoft Technology and Research. Dr. Peter Lee is a corporate vice president who was recently placed in charge of the Microsoft Research Labs.

3. “Build Python Apps in Azure Faster with Visual Studio Code” by Nicolas Garfinkel. This is a low-level presentation that walks through a demo step-by-step, with an emphasis on the Visual Studio Code integrated development environment (IDE). Visual Studio Code is a relatively simple open source IDE that was created in part as a response to the size and complexity of Visual Studio. This presentation provides a very good explanation of exactly what Visual Studio Code is and how it can be used to develop ML/AI systems.

4. “How to Explain Text Models with InterpretML – Deep Dive” by Minsoo Thigpen. An interesting area of ML/AI is model interpretability. A classic example is a scenario where some sort of ML model is used, in part, to approve or deny a loan application. If a loan application is denied, it’s natural to want to understand why the ML model didn’t approve the loan request. Minsoo shows a demo of InterpretML, an open source Python library that can show how the prediction model changes for different subsets of data and can calculate measure of importance of the input predictors.

5. “Teaching Industrial Autonomous Systems” by Scott Stanfield. This presentation addresses the topic of training industrial robotic systems to perform useful tasks. The highlight of the presentation is a demo of a software system that learns to balance a ping pong ball on a moving circular base.