{"product_id":"the-machine-learning-solutions-architect-handbook-second-edition-practical-strategies-and-best-practices-on-the-ml-lifecycle-system-design-mlops-paperback","title":"The Machine Learning Solutions Architect Handbook - Second Edition: Practical strategies and best practices on the ML lifecycle, system design, MLOps, - Paperback","description":"\u003cp\u003eby \u003cb\u003eDavid Ping\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDesign, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eGo in-depth into the ML lifecycle, from ideation and data management to deployment and scaling\u003c\/li\u003e\n\u003cli\u003eApply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions\u003c\/li\u003e\n\u003cli\u003eUnderstand the generative AI lifecycle, its core technologies, and implementation risks\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eDavid 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.\u003c\/p\u003e\u003cp\u003eYou'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.\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eApply ML methodologies to solve business problems across industries\u003c\/li\u003e\n\u003cli\u003eDesign a practical enterprise ML platform architecture\u003c\/li\u003e\n\u003cli\u003eGain an understanding of AI risk management frameworks and techniques\u003c\/li\u003e\n\u003cli\u003eBuild an end-to-end data management architecture using AWS\u003c\/li\u003e\n\u003cli\u003eTrain large-scale ML models and optimize model inference latency\u003c\/li\u003e\n\u003cli\u003eCreate a business application using artificial intelligence services and custom models\u003c\/li\u003e\n\u003cli\u003eDive into generative AI with use cases, architecture patterns, and RAG\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis 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.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eNavigating the ML Lifecycle with ML Solutions Architecture\u003c\/li\u003e\n\u003cli\u003eExploring ML Business Use Cases\u003c\/li\u003e\n\u003cli\u003eExploring ML Algorithms\u003c\/li\u003e\n\u003cli\u003eData Management for ML\u003c\/li\u003e\n\u003cli\u003eExploring Open-Source ML Libraries\u003c\/li\u003e\n\u003cli\u003eKubernetes Container Orchestration Infrastructure Management\u003c\/li\u003e\n\u003cli\u003eOpen-Source ML Platforms\u003c\/li\u003e\n\u003cli\u003eBuilding a Data Science Environment using AWS ML Services\u003c\/li\u003e\n\u003cli\u003eDesigning an Enterprise ML Architecture with AWS ML Services\u003c\/li\u003e\n\u003cli\u003eAdvanced ML Engineering\u003c\/li\u003e\n\u003cli\u003eBuilding ML Solutions with AWS AI Services\u003c\/li\u003e\n\u003cli\u003eAI Risk Management\u003c\/li\u003e\n\u003cli\u003eBias, Explainability, Privacy, and Adversarial Attacks\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Read Sample option to see further chapters)\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 602\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.22 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e April 15, 2024\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":51776328991008,"sku":"9781805122500","price":64.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0974\/9764\/5344\/files\/9fd385373d0c38ffb94f2d965e92543f.webp?v=1780477219","url":"https:\/\/ebocreations.com\/products\/the-machine-learning-solutions-architect-handbook-second-edition-practical-strategies-and-best-practices-on-the-ml-lifecycle-system-design-mlops-paperback","provider":"The E-Book Oasis LLC","version":"1.0","type":"link"}