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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

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

View More author details

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

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. Please follow the instructions at https://docs.docker.com/compose/install/.
Access to Git on the command line and installed as described at 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.
The latest version of your machine learning installed locally as described in Chapter 3, Your Data Science Workbench.

In the next section, we will describe the structure of our data pipeline, the data sources, and the different steps that we will execute to implement our practical example leveraging MLflow project features...

The rest of the chapter is locked

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Authors (1)

Lauchande

Lauchande

Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.

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