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23.8 MB
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May 29, 2025, 10:59 a.m.
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(Last updated: May 29, 2025, 10:59 a.m.)
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| Code.zip | 8.7 MB |
| Muppala S. Generative AI for Cloud Solutions. Building end-to-end...2025.pdf | 15.2 MB |
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| Uploaded by andryold1 | Size 23.8 MB | Health [ 7 /218 ] | Added 2025-05-29 |
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SOURCE: Muppala S. Generative AI for Cloud Solutions. Building end-to-end...2025
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COVER
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MEDIAINFO
Textbook in PDF format Generative AI is transforming every industry, with applications ranging from creative content generation, simple chatbots, to entirely new ways of engaging with consumers. But there is as much uncertainty as buzz—understanding how to use this technology securely and responsibly, and recognizing what the pitfalls are. In this book, we will put together a complete picture of Generative AI development on modern cloud platforms, covering all stages of building and operating a production-grade solution with consideration for performance, security, governance, and responsibility. Conceptual discussions will be accompanied by functional examples, using working code on Amazon Web Services (AWS) cloud to demonstrate key concepts. We will explore the full lifecycle, from initial model selection and fine-tuning to production deployment, monitoring, and ongoing operation. Key aspects include prompt engineering, data integration techniques, observability, the shared responsibility model, and the full solution lifecycle from design to operation. Additionally, we will discuss recommendations for prioritizing a Generative AI roadmap for organizations and emerging trends in the field. As readers progress, they will gain insights into the future trends of AI and witness its transformative impact across various industries through case studies. By the end of the book, the readers will have a solid understanding of the features of foundational models and their collaboration with cloud computing, enabling them to create innovative, efficient, and ethical AI solutions in diverse cloud-based applications. LangChain is an integration framework that makes it easy to compose generative AI solutions. Besides just calling a foundation model, you can also build solutions for RAG, agents and tool calling, and content safety moderation. LlamaIndex is an integration framework that concentrates on connecting foundation models to data sources. It includes connectors for over a hundred data sources and automates much of the work of indexing and retrieving this data. LangGraph is a is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. AutoGen is an open-source framework that simplifies the development of LLM applications. At its core, AutoGen provides a multi-agent conversation framework, allowing developers to build LLM-based workflows with collaborative, teachable, and personalized agents. The framework's modular agent architecture and conversation-based programming model make it easier for developers to build and reuse components. This benefits end-users by enabling multiple agents to independently learn and work together on their behalf, allowing them to accomplish more with less effort. Amazon Bedrock Guardrails discussed in the context of veracity and robustness are also applicable here. We encourage the readers to play with the Python chatbot code, different configuration parameters for denied topics, prompt attacks, and content filter of the guardrail feature of the Bedrock. What you will learn - Basics of cloud computing and evolution of generative AI. - Complete solution stack for generative AI to address security and performance concerns. - Prompt engineering for improving performance and security concerns. - Framework for the responsible use of AI to judge risks and put safeguards in place. - Advanced fine-tuning smaller models to get effective performance at lower costs. - Integration with data and tools to expand the power of generative AI and handle complex workflows and access new information. Preface Cloud Computing Evolution of Generative AI Cloud Computing and Generative AI Generative AI Stack Design Components, Model Selection, Evaluation, and Model Playgrounds Prompt Engineering Retrieval Augmented Generation Advanced Model Fine-tuning Techniques Model Hosting and Application Frameworks Agentic Workflows Observability and Monitoring Security and Governance Responsible AI Building and Executing a Generative AI Roadmap Generative AI Future and Trends
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