Machine Learning Engineering Aws by Joshua Arvin (32 results)

Paperback. Condition: Very Good. There is a growing need for professionals with experience in working on machine learning engineering requirements along with knowledge of automating complex MLOps pipelines in the cloud. This book will help you explore a variety of AWS services that ML practitioners can use to solve various ML engineering requirements and challenges in production.…

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.

Condition: New.

paperback. Condition: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New. 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 FeaturesBuild 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.…

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Condition: New. pp. 530.

Paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. In English.

Condition: As New. Unread book in perfect condition.

Condition: New.

Paperback. Condition: New. 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 FeaturesBuild 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.…

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 FeaturesBuild 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.…

Condition: New. Print on Demand pp. 530.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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.

Condition: New. PRINT ON DEMAND pp. 530.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condition: New. Print on Demand.

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 FeaturesBuild 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.…

Condition: New. PRINT ON DEMAND.

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 FeaturesBuild 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.…

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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 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…