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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

Making a cloud deployment with AWS SageMaker

In the last few years, services such as AWS SageMaker have been gaining ground as an engine to run ML workloads. MLflow provides integrations and easy-to-use commands to deploy your model into the SageMaker infrastructure. The execution of this section will take several minutes (5 to 10 minutes depending on your connection) due to the need to build large Docker images and push the images to the Docker Registry.

The following is a list of some critical prerequisites for you to follow along:

The AWS CLI configured locally with a default profile (for more details, you can look at https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).
AWS access in the account to SageMaker and its dependencies.
AWS access in the account to push to Amazon Elastic Container Registry (ECR) service.
Your MLflow server needs to be running as mentioned in the first Starting up a local model registry section.

To deploy...

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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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