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Machine Learning Engineering with MLflow

You're reading from  Machine Learning Engineering with MLflow

Product type Book
Published in Aug 2021
Publisher Packt
ISBN-13 9781800560796
Pages 248 pages
Edition 1st Edition
Languages
Author (1):
Natu Lauchande Natu Lauchande
Profile icon Natu Lauchande

Table of Contents (18) Chapters

Preface Section 1: Problem Framing and Introductions
Chapter 1: Introducing MLflow Chapter 2: Your Machine Learning Project Section 2: Model Development and Experimentation
Chapter 3: Your Data Science Workbench Chapter 4: Experiment Management in MLflow Chapter 5: Managing Models with MLflow Section 3: Machine Learning in Production
Chapter 6: Introducing ML Systems Architecture Chapter 7: Data and Feature Management Chapter 8: Training Models with MLflow Chapter 9: Deployment and Inference with MLflow Section 4: Advanced Topics
Chapter 10: Scaling Up Your Machine Learning Workflow Chapter 11: Performance Monitoring Chapter 12: Advanced Topics with MLflow Other Books You May Enjoy

Section 2: Model Development and Experimentation

This section covers the process of end-to-end model development with MLflow, including experimentation management through MLflow, targeting the algorithm development phase in machine learning. You will learn how to use MLflow to iteratively develop your machine learning model and manage it with MLflow.

The following chapters are covered in this section:

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Machine Learning Engineering with MLflow
Published in: Aug 2021 Publisher: Packt ISBN-13: 9781800560796
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