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The machine learning solutions architect handbook / David Ping

By: Material type: TextPublication details: Birmingham : Packt Publishing, 2024Edition: 2nd edDescription: xxii, 569 pISBN:
  • 9781805122500
Subject(s): DDC classification:
  • 006.31 PIN-M
Summary: 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 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.
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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 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.

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