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Machine Learning Solutions Architect by David Ping (34 results)
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The Machine Learning Solutions Architect Handbook - Second Edition: Practical strategies and best practices on the ML lifecycle, system design, MLOps, (Paperback or Softback)
Published by Packt Publishing 4/15/2024, 2024
ISBN 10: 1805122509 ISBN 13: 9781805122500
Language: English
Paperback or Softback. Condition: New. The Machine Learning Solutions Architect Handbook - Second Edition: Practical strategies and best practices on the ML lifecycle, system design, MLOps, 2.25. Book.
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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
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The Machine Learning Solutions Architect Handbook - Second Edition: Practical strategies and best practices on the ML lifecycle, system design, MLOps,
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The Machine Learning Solutions Architect Handbook - Second Edition: Practical strategies and best practices on the ML lifecycle, system design, MLOps,
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The Machine Learning Solutions Architect Handbook
Published by Packt Publishing Limited, 2024
ISBN 10: 1805122509 ISBN 13: 9781805122500
Language: English
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The Machine Learning Solutions Architect Handbook - Second Edition
Published by Packt Publishing Limited, 2024
ISBN 10: 1805122509 ISBN 13: 9781805122500
Language: English
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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
Published by Packt Publishing Limited, 2024
ISBN 10: 1805122509 ISBN 13: 9781805122500
Language: English
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The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting
Published by Packt Publishing 2022-01, 2022
ISBN 10: 1801072167 ISBN 13: 9781801072168
Language: English
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The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting
Published by Packt Publishing (edition ), 2022
ISBN 10: 1801072167 ISBN 13: 9781801072168
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWSPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey FeaturesGo in-depth into the ML lifecycle, from ideation and data management to deployment and scalingApply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutionsUnderstand the generative AI lifecycle, its core technologies, and implementation risksBook DescriptionDavid 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 learnApply ML methodologies to solve business problems across industriesDesign a practical enterprise ML platform architectureGain an understanding of AI risk management frameworks and techniquesBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using artificial intelligence services and custom modelsDive into generative AI with use cases, architecture patterns, and RAGWho this book is forThis book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.Table of ContentsNavigating the ML Lifecycle with ML Solutions ArchitectureExploring ML Business Use CasesExploring ML AlgorithmsData Management for MLExploring Open-Source ML LibrariesKubernetes Container Orchestration Infrastructure ManagementOpen-Source ML PlatformsBuilding a Data Science Environment using AWS ML ServicesDesigning an Enterprise ML Architecture with AWS ML ServicesAdvanced ML EngineeringBuilding ML Solutions with AWS AI ServicesAI Risk ManagementBias, Explainability, Privacy, and Adversarial Attacks(N.B. Please use the Read Sample option to see further chapters).
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The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting
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The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting
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The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting
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Add to basketCondition: New. As machine learning becomes increasingly important across many industries, organizations need to keep up by building secure and scalable ML platforms. This handbook takes you through the whole process, from data science to system architecture and ML governa.
