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Machine Learning Engineering in Action
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Machine Learning Engineering in Action
by Ben Wilson
Machine Learning Engineering in Action
inside front cover
Machine Learning Engineering in Action
Copyright
contents
front matter
Part 1 An introduction to machine learning engineering
1 What is a machine learning engineer?
2 Your data science could use some engineering
3 Before you model: Planning and scoping a project
4 Before you model: Communication and logistics of projects
5 Experimentation in action: Planning and researching an ML project
6 Experimentation in action: Testing and evaluating a project
7 Experimentation in action: Moving from prototype to MVP
8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization
Part 2 Preparing for production: Creating maintainable ML
9 Modularity for ML: Writing testable and legible code
10 Standards of coding and creating maintainable ML code
11 Model measurement and why it’s so important
12 Holding on to your gains by watching for drift
13 ML development hubris
Part 3 Developing production machine learning code
14 Writing production code
15 Quality and acceptance testing
16 Production infrastructure
Appendix A. Big O(no) and how to think about runtime performance
Appendix B. Setting up a development environment
index
inside back cover
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