The Machine Learning Solutions Architect Handbook, Second Edition

eBook Details:

• Paperback: 602 pages
• Publisher: WOW! eBook (April 15, 2024)
• Language: English
• ISBN-10: 1805122509
• ISBN-13: 978-1805122500

eBook Description:

The Machine Learning Solutions Architect Handbook, 2nd 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.

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 The Machine Learning Solutions Architect Handbook, Second Edition is the exploration of ML risk management and a deep understanding of the different stages of AI/ML adoption.

• 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

By the end of this The Machine Learning Solutions Architect Handbook, 2nd Edition, 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.