Generative AI with Amazon Bedrock : Build, scale, and secure generative AI applications using Amazon Bedrock
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| Support: | E-Book |
| Langue: | Anglais |
| Publié: |
Birmingham :
Packt Publishing.
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| Autres localisations: | Voir dans le Sudoc |
| Résumé: | Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standards. Key Features: Learn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist Architects ; Master the core techniques to develop and deploy several AI applications at scale ; Go beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricks ; Purchase of the print or Kindle book includes a free PDF eBook. Book Description: The concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You'll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you'll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization. What you will learn: Explore the generative AI landscape and foundation models in Amazon BedrockFine-tune generative models to improve their performance ; Explore several architecture patterns for different business use cases ; Gain insights into ethical AI practices, model governance, and risk mitigation strategies ; Enhance your skills in employing agents to develop intelligence and orchestrate tasks ; Monitor and understand metrics and Amazon Bedrock model response ; Explore various industrial use cases and architectures to solve real-world business problems using RAGStay on top of architectural best practices and industry standards. Who this book is for: This book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected |
| Accès en ligne: | Accès à l'E-book |
| Résumé: | Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standards. Key Features: Learn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist Architects ; Master the core techniques to develop and deploy several AI applications at scale ; Go beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricks ; Purchase of the print or Kindle book includes a free PDF eBook. Book Description: The concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You'll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you'll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization. What you will learn: Explore the generative AI landscape and foundation models in Amazon BedrockFine-tune generative models to improve their performance ; Explore several architecture patterns for different business use cases ; Gain insights into ethical AI practices, model governance, and risk mitigation strategies ; Enhance your skills in employing agents to develop intelligence and orchestrate tasks ; Monitor and understand metrics and Amazon Bedrock model response ; Explore various industrial use cases and architectures to solve real-world business problems using RAGStay on top of architectural best practices and industry standards. Who this book is for: This book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected |
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| Description: | Couverture (https://static2.cyberlibris.com/books_upload/136pix/9781804618585.jpg). |
| ISBN: | 9781804618585 |
| Accès: | L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement |