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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

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Rohith Oct 25, 2023

Subscriber review

Chelsy Apr 17, 2024

Thanks David!

Amazon Verified review

Kitty May 22, 2024

As a university CISO, the top concerns regarding AI/ML evolved around data security, ethical use, and governance. • Ensuring the security of data used by AI/ML systems is paramount, as these technologies can process vast amounts of sensitive information. The CISO must ensure that data privacy regulations are adhered to and that the AI/ML systems are protected against cyber threats. • The ethical use of AI/ML is a significant concern. This includes issues such as the potential for bias in AI-generated content and the need for transparency in how AI models are trained and deployed. • Governance of AI/ML involves establishing policies and frameworks that dictate how AI/ML technologies are utilized within the university. This includes setting up clear guidelines for responsible AI use, monitoring compliance, and managing the risks associated with these powerful tools.This book clearly outlines and provide guidelines to help addresses the concerns I have as a university CISO

Felipe Lopez Apr 15, 2024

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.

Dr John Jones Jun 13, 2024

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.

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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