You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

View More author details

Table of Contents (18) Chapters

Preface

1. Section 1: Framework for Building Machine Learning Models

2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER

3. Chapter 2: Characterizing Your Machine Learning Problem

4. Chapter 3: Code Meets Data

5. Chapter 4: Machine Learning Pipelines

6. Chapter 5: Model Evaluation and Packaging

7. Section 2: Deploying Machine Learning Models at Scale

8. Chapter 6: Key Principles for Deploying Your ML System

9. Chapter 7: Building Robust CI/CD Pipelines

10. Chapter 8: APIs and Microservice Management

11. Chapter 9: Testing and Securing Your ML Solution

12. Chapter 10: Essentials of Production Release

13. Section 3: Monitoring Machine Learning Models in Production

14. Chapter 11: Key Principles for Monitoring Your ML System

15. Chapter 12: Model Serving and Monitoring

16. Chapter 13: Governing the ML System for Continual Learning

17. Other Books You May Enjoy

Setting up the resources and tools

If you have these tools already installed and set up on your PC, feel free to skip this section; otherwise, follow the detailed instructions to get them up and running.

Installing MLflow

We get started by installing MLflow, which is an open source platform for managing the ML life cycle, including experimentation, reproducibility, deployment, and a central model registry.

To install MLflow, go to your terminal and execute the following command:

pip3 install mlflow

After successful installation, test the installation by executing the following command to start the mlflow tracking UI:

mlflow ui

Upon running the mlflow tracking UI, you will be running a server listening at port 5000 on your machine, and it outputs a message like the following:

[2021-03-11 14:34:23 +0200] [43819] [INFO] Starting gunicorn 20.0.4
[2021-03-11 14:34:23 +0200] [43819] [INFO] Listening at: http://127.0.0.1:5000 (43819)
[2021-03-11 14:34:23 +0200...