The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
by David Ping
The Machine Learning Solutions Architect Handbook provides practical strategies for navigating the machine learning lifecycle. It explores system design, MLOps, and generative AI, giving readers a broad foundation for planning, building, and managing machine learning solutions in real-world technical environments.
About This Book
The Machine Learning Solutions Architect Handbook presents practical strategies and best practices for working across the machine learning lifecycle.
It focuses on system design and the considerations involved in building effective machine learning solutions.
The book also covers MLOps, offering guidance on operational practices for machine learning systems.
Generative AI is included as part of this broad, practical treatment of modern machine learning architecture.
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