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

Using the workbench for stock prediction

In this section, we will use the workbench step by step to set up a new project. Follow the instructions step by step to start up your environment and use the workbench for the stock-prediction project.

Important note

It is critical that all packages/libraries listed in the Technical requirements section are correctly installed on your local machine to enable you to follow along.

Starting up your environment

We will move on next to exploring your own development environment, based on the development environment shown in this section. Please execute the following steps:

Copy the contents of the project available in https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow/tree/master/Chapter03/gradflow.
Start your local environment by running the following command:
```
make
```
Inspect the created environments, like this:
```
$ docker ps 
```
The following screenshot presents three Docker images: the first for Jupyter...

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

Table of Contents (18) Chapters

Using the workbench for stock prediction

Starting up your environment

Authors (1)

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

Table of Contents (18) Chapters

Using the workbench for stock prediction

Starting up your environment

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Authors (1)

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