Introducing MLOps cover
Introducing MLOps
by Mark Treveil, Dataiku team
Description: Introducing MLOps by Mark Treveil and the Dataiku team explains processes and practices for scaling machine learning projects effectively within enterprises. It addresses challenges in deploying and managing ML models beyond initial development
ISBN: 9781492083283
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paeselhz · 2023-11-20 · Original thread
I did the Deep Learning Specialization on MLOps which is available on Coursera (https://www.deeplearning.ai/courses/machine-learning-enginee...), however, I found it too "Tensorflow-oriented" and it did not match the current development stack of the team that I was working on.

Most of the knowledge I was able to glue together came from webinars/lives/tutorials/books I found that already solved the same problems I was looking for.

If you have a solid Python foundation and already have some experience in Machine Learning in general, I would recommend the Practical MLOps book( https://www.oreilly.com/library/view/practical-mlops/9781098...), or even Introducing MLOps (https://www.oreilly.com/library/view/introducing-mlops/97814...) as starting points.

EDIT: Also, nothing beats a good project end-to-end, where you take a toy problem, and try to build an entire stack around it, from the training of simple ML models, tracking the models' versions, creating APIs to serve these models, monitoring, and so on.