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The Machine Learning Solutions Architect Handbook - Second Edition

You're reading from  The Machine Learning Solutions Architect Handbook - Second Edition

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Pages 602 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
David Ping David Ping
Profile icon David Ping
LinkedIn
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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Table of Contents (19) Chapters

Preface 1. Navigating the ML Lifecycle with ML Solutions Architecture 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.

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Authors (1)

Profile icon David Ping
LinkedIn
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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See other products by David Ping

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