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The Machine Learning Solutions Architect Handbook
The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI , Second Edition

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Profile Icon David Ping
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$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (19 Ratings)
Paperback Apr 2024 602 pages 2nd Edition
eBook
$39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon David Ping
Arrow right icon
$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (19 Ratings)
Paperback Apr 2024 602 pages 2nd Edition
eBook
$39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling
  • Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions
  • Understand the generative AI lifecycle, its core technologies, and implementation risks

Description

David Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills. You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI. By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.

Who is this book for?

This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.

What you will learn

  • Apply ML methodologies to solve business problems across industries
  • Design a practical enterprise ML platform architecture
  • Gain an understanding of AI risk management frameworks and techniques
  • Build an end-to-end data management architecture using AWS
  • Train large-scale ML models and optimize model inference latency
  • Create a business application using artificial intelligence services and custom models
  • Dive into generative AI with use cases, architecture patterns, and RAG

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 15, 2024
Length: 602 pages
Edition : 2nd
Language : English
ISBN-13 : 9781805122500
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Amazon
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Product Details

Publication date : Apr 15, 2024
Length: 602 pages
Edition : 2nd
Language : English
ISBN-13 : 9781805122500
Vendor :
Amazon
Category :
Languages :
Tools :

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Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(19 Ratings)
5 star 78.9%
4 star 10.5%
3 star 0%
2 star 5.3%
1 star 5.3%
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Felipe Lopez Apr 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Purchasing 'The Machine Learning Solutions Architect Handbook' was an absolute game-changer for me. This concise yet comprehensive guide brilliantly covers the intricacies of building and deploying ML workflows in the cloud.Whether you're a cloud architect venturing into ML or a data scientist aiming to operationalize workflows, this book is indispensable. It blends theory with practical exercises in a way that caters to both beginners and seasoned professionals. The inclusion of use cases and an overview of common technologies ensures a holistic understanding, while practical exercises facilitate quick learning. As a Machine Learning Solutions Architect, I'll be keeping this handbook close at hand, ready to revisit its invaluable insights time and again.
Amazon Verified review Amazon
Dr John Jones Jun 13, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've just finished reading this book and what a great read and reference book it is. It is packed with essential ideas and information for the machine learning lifecycle. With my AWS background, it felt incredibly familiar yet practical, covering all aspects of the machine learning lifecycle.Given all the GenAI hype, I particularly enjoyed Chapter 15, "Navigating the Generative AI Project Lifecycle"; David touches on the foundations of generative AI and covers details around generative AI platforms, retrieval-augmented generation (RAG) architecture, as well as practical applications across industries.From foundational ML algorithms to advanced tools and architectures, this book caters to readers at various expertise levels in a readable manner. He covers real-life applications and best practices: sections on robust ML infrastructure, optimisation methods, and AWS frameworks like WAF and CAF provide actionable insights for real-world applications.► Ideal AudienceThis book is an excellent addition for machine learning practitioners, solutions architects, data scientists/engineers implementing advanced AI, and tech leaders/decision-makers seeking strategic implications of ML and AI for their organisations.
Amazon Verified review Amazon
bubububu Apr 23, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
With a focus on practical strategies and best practices, readers will gain valuable insights into solving real-world business problems with AWS. From ML lifecycle to system design, this book covers it all. Highly recommended for anyone looking to elevate their expertise!"
Amazon Verified review Amazon
Shikhar May 20, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Thank you David Ping for pouring your insightful contributions and expertise to this ever-evolving field.This book comprehensively covers best practices around:- applying ML technologies to solve cross industry problems- designing enterprise grade ML platform architecture- training large scale ML models and optimize model inferency- diving into generative AI with RAG and diverse architecture patterns!
Amazon Verified review Amazon
CCLin Jun 16, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a must read for any new or existing solutions architects looking to upgrade their knowledge and keep their skills sharp in a rapidly evolving AI/ML arena. The scope and depth of David’s knowledge is unparalleled. It is one of the best resources that focuses on the principles behind designing and implementing AI/ML systems for real world business application. An indispensable resource to help navigate the ever-changing landscape of AI/ML adaptation.
Amazon Verified review Amazon
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