The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI, Second Edition
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
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.
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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David Ping is a senior technology leader with over 25 years of experience in the technology and financial services industry. His technology focus areas include cloud architecture, enterprise ML platform design, large-scale model training, intelligent document processing, intelligent media processing, intelligent search, and data platforms. He currently leads an AI/ML solutions architecture team at AWS, where he helps global companies design and build AI/ML solutions in the AWS cloud. Before joining AWS, David held various senior technology leadership roles at Credit Suisse and JPMorgan. He started his career as a software engineer at Intel. David has an engineering degree from Cornell University.
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