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The Machine Learning Solutions Architect Handbook

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

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Length 602 pages
Edition 2nd Edition
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Author (1):
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David Ping David Ping
Author Profile Icon David Ping
David Ping
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Table of Contents (19) Chapters Close

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

Building a Data Science Environment Using AWS ML Services

While some organizations opt to build their own ML platforms using open-source technologies, many other organizations prefer to leverage fully managed ML services as the foundation for their ML platforms. In this chapter, we will delve into the fully managed ML services offered by AWS. Specifically, you will learn about Amazon SageMaker, and other related services for building a data science environment for data scientists. We will examine various components of SageMaker, such as SageMaker Studio, SageMaker Training, and SageMaker Hosting. Additionally, we will delve into the architectural framework for constructing a data science environment and provide a hands-on exercise to guide you through the process.

In a nutshell, this chapter will cover the following topics:

  • SageMaker overview
  • Data science environment architecture using SageMaker
  • Best practices for building a data science environment
  • ...
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