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Machine Learning Engineering with MLflow

You're reading from   Machine Learning Engineering with MLflow Manage the end-to-end machine learning life cycle with MLflow

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800560796
Length 248 pages
Edition 1st Edition
Tools
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Author (1):
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Natu Lauchande Natu Lauchande
Author Profile Icon Natu Lauchande
Natu Lauchande
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Problem Framing and Introductions
2. Chapter 1: Introducing MLflow FREE CHAPTER 3. Chapter 2: Your Machine Learning Project 4. Section 2: Model Development and Experimentation
5. Chapter 3: Your Data Science Workbench 6. Chapter 4: Experiment Management in MLflow 7. Chapter 5: Managing Models with MLflow 8. Section 3: Machine Learning in Production
9. Chapter 6: Introducing ML Systems Architecture 10. Chapter 7: Data and Feature Management 11. Chapter 8: Training Models with MLflow 12. Chapter 9: Deployment and Inference with MLflow 13. Section 4: Advanced Topics
14. Chapter 10: Scaling Up Your Machine Learning Workflow 15. Chapter 11: Performance Monitoring 16. Chapter 12: Advanced Topics with MLflow 17. Other Books You May Enjoy

Section 4: Advanced Topics

This section covers assorted topics on advanced usage of MLflow, allowing you to learn how to scale MLflow in big data environments and to meet high computing demands. We will cover advanced use cases to ensure broad coverage of the type of models and use cases to be discussed, alongside MLflow internals and how to contribute to it.

The following chapters are covered in this section:

  • Chapter 10, Scaling Up Your Machine Learning Workflow
  • Chapter 11, Performance Monitoring
  • Chapter 12, Advanced Topics with MLFlow
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