For the last couple of months, I have been doing some research on ML in production.

I have shared a few resources here, from repositories to blogs.

However, I have found some of the best content in books.

If anyone is interested, here is a list of books I am studying

Designing Data-Intensive Applications

by Martin Kleppmann

Building Machine Learning Pipelines

By Hannes Hapke and Catherine Nelson

Building Machine Learning Powered Applications

by Emmanuel Ameisen

Introducing MLOps: How to Scale Machine Learning
in the Enterprise

by Clément Stenac, Léo Dreyfus-Schmidt, Kenji Lefèvre, Nicolas Omont, and Mark Treveil

(it's in early release)

High Performance Python

by Micha Gorelick and Ian Ozsvald

Machine Learning Systems: Designs that scale

by Jeff Smith

Python for DevOps

by Noah Gift, Kennedy Behrman, Alfredo Deza, and Grig Gheorghiu

Building Machine Learning and Deep Learning Models on Google Cloud Platform

by Ekaba Bisong

Data Management at Scale

by Piethein Strengholt

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

by Valliappa Lakshmanan, Sara Robinson, and Michael Munn

(it's in early release)

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD

by Jeremy Howard and Sylvain Gugger

Kubeflow Operations Guide: Managing On-Premises, Cloud, and Hybrid Deployment

by Josh Patterson, Michael Katzenellenbogen,
and Austin Harris

(it's in early release)

Agile Machine Learning

by Eric Carter and Matthew Hurst

Happy reading!

Feel free to add any books you found useful on the topic of machine learning in production.