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
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

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)
in the Enterprise
by Clément Stenac, Léo Dreyfus-Schmidt, Kenji Lefèvre, Nicolas Omont, and Mark Treveil
(it's in early release)
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)
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
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)
by Josh Patterson, Michael Katzenellenbogen,
and Austin Harris
(it's in early release)
Happy reading!
Feel free to add any books you found useful on the topic of machine learning in production.
Feel free to add any books you found useful on the topic of machine learning in production.