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

Summary

In this chapter, we have explored various ML algorithms that can be applied to solve different types of ML problems. By now, you should have a good understanding of which algorithms are suitable for which specific tasks. Additionally, you have set up a basic data science environment on your local machine, utilized the scikit-learn ML libraries to analyze and preprocess data, and successfully trained an ML model.

In the upcoming chapter, our focus will shift to the intersection of data management and the ML lifecycle. We will delve into the significance of effective data management and discuss how to build a comprehensive data management platform on Amazon Web Services (AWS) to support downstream ML tasks. This platform will provide the necessary infrastructure and tools to streamline data processing, storage, and retrieval, ultimately enhancing the overall ML workflow.