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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 Navigating the ML Lifecycle with ML Solutions Architecture Exploring ML Business Use Cases Exploring ML Algorithms Data Management for ML Exploring Open-Source ML Libraries Kubernetes Container Orchestration Infrastructure Management Open-Source ML Platforms Building a Data Science Environment Using AWS ML Services Designing an Enterprise ML Architecture with AWS ML Services Advanced ML Engineering Building ML Solutions with AWS AI Services AI Risk Management Bias, Explainability, Privacy, and Adversarial Attacks Charting the Course of Your ML Journey Navigating the Generative AI Project Lifecycle Designing Generative AI Platforms and Solutions Other Books You May Enjoy
Index

Summary

Throughout this chapter, we have explored various industries and the ways in which they are utilizing ML to solve business challenges and drive growth. From finance and healthcare to retail and automotive, we have seen how ML can improve processes, generate insights, and enhance the customer experience. The examples within this chapter have hopefully sparked ideas you can now bring to stakeholders to kickstart an ML roadmap discussion and think creatively about the potential high-impact applications in your own organizations.

As we move into the next chapter, we will delve deeper into the mechanics of ML, exploring the fundamental concepts behind how machines learn and some of the most widely used algorithms in the field. This will provide you with a solid foundation for understanding how ML is applied in practice to solve various ML problems.

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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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