Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800560796
Length 248 pages
Edition 1st Edition
Tools
Arrow right icon
Author (1):
Arrow left icon
Natu Lauchande Natu Lauchande
Author Profile Icon Natu Lauchande
Natu Lauchande
Arrow right icon
View More author details
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

Chapter 2: Your Machine Learning Project

The approach of this book is to iterate through a practical business project – namely, stock market prediction – and, with this use case, explore through the different chapters the different features of MLflow. We will use a structured approach to frame a machine learning problem and project. A sample pipeline will be created and used to iterate and evolve the project in the remainder of the book.

Using a structured framework to describe a machine learning problem helps the practitioner to reason more efficiently about the different requirements of the machine learning pipeline. We will present a practical pipeline using the requirements elicited during framing.

Specifically, we will cover the following sections in this chapter: 

  • Exploring the machine learning process
  • Framing the machine learning problem
  • Introducing the stock market prediction problem
  • Developing your machine learning baseline...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at R$50/month. Cancel anytime