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

Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook.

Key Features

• Solve large-scale ML challenges in the cloud with several open-source and AWS tools and frameworks
• Apply risk management techniques in the ML life cycle and learn architecture patterns for solutions
• Understand the challenges and risks of implementing generative AI

Book Description

David Ping, Head of GenAI and ML Solution Architecture 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 life cycle 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 generative AI, the biggest new addition to the handbook is the exploration of ML risk management and a deep understanding of the different stages of AI/ML adoption.

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.

What you will learn

• Apply ML methodologies to solve business problems
• 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 AI services and custom models
• Dive into generative AI with use cases, architecture patterns, and RAG

Who this book is 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.

Table of Contents

1. Machine Learning and Machine Learning Solutions Architecture
2. Business Use Cases for Machine Learning
3. Machine Learning Algorithms
4. Data Management for Machine Learning
5. Open-Source Machine Learning Libraries
6. Kubernetes Container Orchestration Infrastructure
7. Open-Source ML Platforms
8. Building a Data Science Environment using AWS ML Services
9. Building Enterprise ML Architecture with AWS ML Services
10. Advanced ML Engineering
11. Building ML solutions with AWS AI Services
12. ML Risk Management
13. Bias, Explainability, Privacy, and Adversarial Attacks
14. Progressing Through the ML Journey
15. ML Milestones and Research Trends
16. Designing Generative AI Platform and Solutions