You're reading from The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781805122500

Length 602 pages

Edition 2nd Edition

Languages

Python

Tools

MLOps

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (19) Chapters

Preface

1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER

2. Exploring ML Business Use Cases

3. Exploring ML Algorithms

4. Data Management for ML

5. Exploring Open-Source ML Libraries

6. Kubernetes Container Orchestration Infrastructure Management

7. Open-Source ML Platforms

8. Building a Data Science Environment Using AWS ML Services

9. Designing an Enterprise ML Architecture with AWS ML Services

10. Advanced ML Engineering

11. Building ML Solutions with AWS AI Services

12. AI Risk Management

13. Bias, Explainability, Privacy, and Adversarial Attacks

14. Charting the Course of Your ML Journey

15. Navigating the Generative AI Project Lifecycle

16. Designing Generative AI Platforms and Solutions

17. Other Books You May Enjoy

18. Index

Best practices for building a data science environment

Data science environments are meant for data scientists to perform quick experimentations using a wide range of ML frameworks and libraries. The following are some best practices to follow when providing such an environment for your data scientists:

Run large-scale model training using the SageMaker Training service instead of Studio notebooks: SageMaker Studio notebooks are meant for quick experimentation with small datasets. While it is possible to provision large EC2 instances for certain large model training jobs, it is not cost effective to always keep a large EC2 instance running for a notebook all the time.
Abstract infrastructure configuration details from data scientists: There are many infrastructure configurations to consider when using SageMaker, such as networking configuration, IAM roles, encryption keys, EC2 instance types, and storage options. To make the lives of data scientists easier, abstract...