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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

Technical requirements 

For this chapter, you will need the following prerequisites: 

  • The latest version of Docker installed on your machine. If you don’t already have it installed, please follow the instructions at https://docs.docker.com/get-docker/.

    The latest version of Docker Compose installed. If you don’t already have it installed, please follow the instructions at https://docs.docker.com/compose/install/.

  • Access to Git in the command line, and installed as described in this Uniform Resource Locator (URL): https://git-scm.com/book/en/v2/Getting-Started-Installing-Git.
  • Access to a bash terminal (Linux or Windows). 
  • Access to a browser.
  • Python 3.5+ installed.
  • MLflow installed locally, as described in Chapter 1, Introducing MLflow.
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