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

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Autor:
David Ping
The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition David Ping - okladka książki

The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition David Ping - okladka książki

The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition David Ping - audiobook MP3

The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition David Ping - audiobook CD

Serie wydawnicze:
Learning
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
602
Dostępne formaty:
     PDF
     ePub

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Do przechowalni

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.

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O autorze książki

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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