Table of Contents
Preface
Part 1: The Challenges of Adopting ML and Understanding MLOps (What and Why)
Chapter 1: Challenges in Machine Learning
Understanding ML
Delivering ML value
Choosing the right approach
The importance of data
Facing the challenges of adopting ML
Focusing on the big picture
Breaking down silos
Fail-fast culture
An overview of the ML platform
Summary
Further reading
Chapter 2: Understanding MLOps
Comparing ML to traditional programming
Exploring the benefits of DevOps
Understanding MLOps
ML
DevOps
ML project life cycle
Fast feedback loop
Collaborating over the project life cycle
The role of OSS in ML projects
Running ML projects on Kubernetes
Summary
Further reading
Chapter 3: Exploring Kubernetes
Technical requirements ...
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