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The Machine Learning Solutions Architect Handbook

You're reading from The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072168

Length 442 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture

2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture FREE CHAPTER

3. Chapter 2: Business Use Cases for Machine Learning

4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning

5. Chapter 3: Machine Learning Algorithms

6. Chapter 4: Data Management for Machine Learning

7. Chapter 5: Open Source Machine Learning Libraries

8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management

9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

10. Chapter 7: Open Source Machine Learning Platforms

11. Chapter 8: Building a Data Science Environment Using AWS ML Services

12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

13. Chapter 10: Advanced ML Engineering

14. Chapter 11: ML Governance, Bias, Explainability, and Privacy

15. Chapter 12: Building ML Solutions with AWS AI Services

16. Other Books You May Enjoy

Technical requirements

In this chapter, you will need access to an AWS account and AWS services such as Amazon S3, Amazon Lake Formation, AWS Glue, and AWS Lambda. If you do not have an AWS account, follow the official AWS website's instructions to create an account.

The rest of the chapter is locked

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Authors (1)

David Ping

David Ping

David Ping is an accomplished author and industry expert with over 28 years of experience in the field of data science and technology. He currently serves as the leader of a team of highly skilled data scientists and AI/ML solutions architects at AWS. In this role, he assists organizations worldwide in designing and implementing impactful AI/ML solutions to drive business success. David's extensive expertise spans a range of technical domains, including data science, ML solution and platform design, data management, AI risk, and AI governance. Prior to joining AWS, David held positions in renowned organizations such as JPMorgan, Credit Suisse, and Intel Corporation, where he contributed to the advancements of science and technology through engineering and leadership roles. With his wealth of experience and diverse skill set, David brings a unique perspective and invaluable insights to the field of AI/ML.

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