The Machine Learning Solutions Architect Handbook: Practical strategies and best

Description: The Machine Learning Solutions Architect Handbook by David Ping Estimated delivery 3-12 business days Format Paperback Condition Brand New Description 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 eBookKey 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.Youll 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. Youll 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 , youll 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. Youll 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. Author Biography David Ping is an accomplished author and industry expert with over 28 years of experience in the field of data science and technology. He currently serves as the leader of a team of highly skilled data scientists and AI/ML solutions architects at AWS. In this role, he assists organizations worldwide in designing and implementing impactful AI/ML solutions to drive business success. Davids extensive expertise spans a range of technical domains, including data science, ML solution and platform design, data management, AI risk, and AI governance. Prior to joining AWS, David held positions in renowned organizations such as JPMorgan, Credit Suisse, and Intel Corporation, where he contributed to the advancements of science and technology through engineering and leadership roles. With his wealth of experience and diverse skill set, David brings a unique perspective and invaluable insights to the field of AI/ML. Details ISBN 1805122509 ISBN-13 9781805122500 Title The Machine Learning Solutions Architect Handbook Author David Ping Format Paperback Year 2024 Pages 602 Edition 2nd Publisher Packt Publishing Limited GE_Item_ID:159286985; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys

Price: 52.75 USD

Location: Fairfield, Ohio

End Time: 2024-12-01T07:07:29.000Z

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ISBN-13: 9781805122500

Book Title: The Machine Learning Solutions Architect Handbook

Language: English

Publication Name: Machine Learning Solutions Architect Handbook : Practical Strategies and Best Practices on the ML Lifecycle, System Design, MLOps, and Generative AI

Publisher: Packt Publishing, The Limited

Subject: Machine Theory, General

Publication Year: 2024

Type: Textbook

Subject Area: Computers, Science

Author: David Ping

Item Length: 92.5 in

Item Width: 75 in

Format: Trade Paperback