FaridKhan Posted April 17 Report Share Posted April 17 English | 2024 | ISBN: 9781805122500 | 752 pages | True EPUB | 19.58 MBSubject: COM037000 - COMPUTERS / Machine Theory, COM062000 - COMPUTERS / Data Modeling & Design, COM048000 - COMPUTERS / Systems Architecture / Distributed Systems & ComputingPublisher: PacktDescription: The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI, 2nd EditionDesign, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Key FeaturesSolve large-scale ML challenges in the cloud with several open-source and AWS tools and frameworksApply risk management techniques in the ML life cycle and learn architecture patterns for solutionsUnderstand the challenges and risks of implementing generative AI Book 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 RAG Who 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. Contents of Download: David Ping The Machine Learning Solutions Architect Handbook.Epub.epub (David Ping) (19.58 MB)️ The Machine Learning Solutions Architect Handbook Final,2nd Edition (19.58 MB)NitroFlare Link(s)https://nitroflare.com/view/9BF10C605B53CA7/The.Machine.Learning.Solutions.Architect.Handbook.Final2nd.Edition.rarRapidGator Link(s)https://rapidgator.net/file/5c076d4e940f8565864ee1bec0fcdfcb/The.Machine.Learning.Solutions.Architect.Handbook.Final2nd.Edition.rar Link to comment Share on other sites More sharing options...
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