O’Reilly book by Ted Dunning and Ellen Friedman
The book is free, because they are hoping it will serve as good marketing material for MapR Convergent Data Platform.
Focuses on the larger issue of “Machine Learning is more than just developing a model and getting it into production.”
Talks about the need for a holistic approach to the cross-cutting concerns of ML that we will call “machine learning logistics” and “model management.”
Talks about the whole system from start to finish
The book may be a bit dated, as it was published in 2017.
The book itself provides useful content. It’s not just marketing material. Every now and then, you see “tools like MapR Convergent Data Platform”, etc.

Chapter 02

Logistics and model management need to be flexible to handle many different use cases and scenarios.
But you don’t want it to be so complicated that it is also a hindrance to people getting work done.

“The rendezvous architecture takes advantage of data streams and geo-distributed stream replication to maintain a responsive and flexible way to collect and save data, including raw data, and to make data and multiple models available when and where needed.”
“The design strongly supports ongoing model evaluation and multi-model comparison. It’s a new approach to managing models that reduces the burden of logistics while providing exceptional levels of monitoring so that you know what’s happening.”

Machine Learning Logistics Book

“Model management” is a term for the holistic view of the entire machine learning system.
It’s not the cool part of Machine Learning, but it’s extremely important.
New practitioners are going to need some practical advice on this matter.