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

Product type Paperback

Published in Aug 2021

Publisher Packt

ISBN-13 9781800560796

Length 248 pages

Edition 1st Edition

Tools

Maven

Concepts

Machine Learning

Author (1):

Natu Lauchande

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Table of Contents (18) Chapters

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

Introducing the stock market prediction problem

The scenario that we will cover in the remaining chapters of the book is of the hypothetical company PsyStock LLC, which provides a platform for amateur traders, providing APIs and UIs to solve different predictions in the context of stock prediction.

As machine learning practitioners and developers, we should be able to build a platform that will allow a team of data scientists to quickly develop, test, and bring into production machine learning projects.

We will apply and frame the problems initially so we can build our platform upon the basis of the definitions of the problems. It should be noted that the problem framing will evolve as we learn more about the problem: the initial framing will give us guidance on the problem spaces that we will be tackling.

The following are the core projects that we will use as references in the rest of the book for machine learning development in MLflow.