This is not the same book as The Math of Machine Learning, also published by the same department at Berkeley, in 2018, and also authored by Garret Thomas. I hope they will add sections on Ensemble Methods (combining multiple techniques), cross-validation, and feature selection, and then it will cover pretty much everything that the beginner should know. Other popular free books, all written by top experts in their fields, include Foundations of Data Science published by Microsoft's ML Research Lab in 2018, and Statistics: New Foundations, Toolbox, and Machine Learning Recipes published by Data Science Central in 2019.

This is going to be the most important focus area for 2018. Most enterprises have done proof of concepts on ML and are looking to realize the full value of their data with full fledged production implementations of the algorithms. The key technologies in this space may be Clipper. Clipper is the state-of-art ML serving system from Rise labs, Berkeley university and uses distributed computing concepts to scale models, containerized model deployment to handle models created in any platform and also performs cross-framework caching and batching to leverage parallel architectures like GPUs. Finally, Clipper can also perform cross-framework model composition using ML techniques like ensembling and multi-armed bandits.