Machine Learning Engineering Aws by Joshua Arvin (31 results)
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Seller: Greenworld Books, arlington, TX, U.S.A.Greenworld Books
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Condition: very_good. Fast Free Shipping â" Very Good condition book with a firm cover and clean pages. Shows normal use and some light wear or limited notes markings. A solid, nice copy to enjoy.
- Softcover
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- Softcover
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Paperback or Softback. Condition: New. Machine Learning Engineering on AWS: Build, scale, and secure machine learning systems and MLOps pipelines in production. Book.
- Softcover
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- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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Paperback or Softback. Condition: New. Machine Learning Engineering on AWS - Second Edition: Build, deploy, and operationalize LLMs, AI agents, and generative AI systems on AWS. Book.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Published by Packt Publishing
- Softcover
Seller: Academic Book Solutions, Medford, NY, U.S.A.Academic Book Solutions
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- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. pp. 530.
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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- Softcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuil…d and scale AI agents using Amazon Bedrock AgentCore and Strands AgentsFine-tune, evaluate, and deploy ML models using Amazon SageMaker AIAutomate LLMOps workflows with SageMaker PipelinesBook DescriptionModern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required"What you will learnBuild and deploy AI agents using Bedrock AgentCore and Strands AgentsDive deep into ML engineering with Amazon SageMaker AIEvaluate model performance using LLM-as-a-judgeExplore advanced model fine-tuning and deployment using SageMaker AIBuild RAG-powered systems using Bedrock Knowledge Bases and S3 VectorsModernize analytics with a managed transactional data lakeAutomate LLMOps pipelines using SageMaker Pipelines and AWS LambdaExplore best practices for building GenAI systems and AI agents on AWSWho this book is forThis book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended. This hands-on book teaches you how to design, build, optimize, and secure generative AI systems and AI agents on AWS. As you progress through the chapters, you'll leverage various AWS services to automate end-to-end LLMOps pipelines. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
- Print on Demand
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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£ 54.44
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Condition: New. Print on Demand pp. 530.
- Softcover
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Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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£ 57.93
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Condition: New. PRINT ON DEMAND pp. 530.
- Softcover
- Print on Demand
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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£ 49.47
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Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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£ 78.43
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- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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£ 47.99
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Paperback. Condition: new. Paperback. Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuil…d and scale AI agents using Amazon Bedrock AgentCore and Strands AgentsFine-tune, evaluate, and deploy ML models using Amazon SageMaker AIAutomate LLMOps workflows with SageMaker PipelinesBook DescriptionModern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required"What you will learnBuild and deploy AI agents using Bedrock AgentCore and Strands AgentsDive deep into ML engineering with Amazon SageMaker AIEvaluate model performance using LLM-as-a-judgeExplore advanced model fine-tuning and deployment using SageMaker AIBuild RAG-powered systems using Bedrock Knowledge Bases and S3 VectorsModernize analytics with a managed transactional data lakeAutomate LLMOps pipelines using SageMaker Pipelines and AWS LambdaExplore best practices for building GenAI systems and AI agents on AWSWho this book is forThis book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
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£ 53.59
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
- Softcover
- Print on Demand
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
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Paperback. Condition: new. Paperback. Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuil…d and scale AI agents using Amazon Bedrock AgentCore and Strands AgentsFine-tune, evaluate, and deploy ML models using Amazon SageMaker AIAutomate LLMOps workflows with SageMaker PipelinesBook DescriptionModern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required"What you will learnBuild and deploy AI agents using Bedrock AgentCore and Strands AgentsDive deep into ML engineering with Amazon SageMaker AIEvaluate model performance using LLM-as-a-judgeExplore advanced model fine-tuning and deployment using SageMaker AIBuild RAG-powered systems using Bedrock Knowledge Bases and S3 VectorsModernize analytics with a managed transactional data lakeAutomate LLMOps pipelines using SageMaker Pipelines and AWS LambdaExplore best practices for building GenAI systems and AI agents on AWSWho this book is forThis book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
More images- Softcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
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£ 55.63
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Taschenbuch. Condition: Neu. Machine Learning Engineering on AWS | Build, scale, and secure machine learning systems and MLOps pipelines in production | Joshua Arvin Lat | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Packt Publishing | EAN 9781803247595 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1,… 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
- Softcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Machine Learning Engineering on AWS - Second Edition | Build, deploy, and operationalize LLMs, AI agents, and generative AI systems on AWS | Joshua Arvin Lat | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781835881088 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Ba…d Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycleKey Features:Gain practical knowledge of managing ML workloads on AWS using Amazon SageMake…r, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description:There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS.By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.What You Will Learn:Find out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for:This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
