You can learn by doing, or you can learn from someone else’s mistakes. This is the case of the later. Certainly useful to avoid the same mistakes.
This one is pretty heavy read. But if you’re into Martin Fowler’s stuff, you know they are ‘the standard’. This one covers what it takes to apply continuous delivery in machine learning model. The CD for machine learning have similarities to software’s CD, but there are few keys differences as well.
If you want to start learning Machine Learning but don’t know where to start, ML.NET is a good starter. With familiarity of C# and .NET, you could pick up ML.NET fairly quickly. This goes into details on how to get started with ML.NET, even covers Auto-ML!
Everyone thinks AI is cool, futuristic and can solve _almost_ all the problems. But not many think about the consequences, side effects and what it would take to build it right. In another word, a responsible AI. This mini-course go over what we need to consider in building AI.
I like how Sumit gives intro to parallel computing, specifically multi processes vs threading, before he went dive into how it’s applicable in Python for modeling. Worth read even if you skip the Python part.