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Enterprise guide for implementing ge...

Edward, Shakuntala Gupta.

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Enterprise guide for implementing generative AI and agentic AI = a practical guide to developing, deploying, and operationalizing AI-driven applications for enterprise use /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enterprise guide for implementing generative AI and agentic AI/ by Shakuntala Gupta Edward, Rahul Bhattacharya, Vikas Sinha.
其他題名:	a practical guide to developing, deploying, and operationalizing AI-driven applications for enterprise use /
作者:	Edward, Shakuntala Gupta.
其他作者:	Bhattacharya, Rahul.
出版者:	Berkeley, CA :Apress : : 2025.,
面頁冊數:	xv, 409 p. :ill., digital ;24 cm.
內容註:	PART - I -- Chapter 1: Introduction: Evolution of AI and Large language models (LLM). PART - II -- Chapter 2: Generative AI in Business. PART - III -- Chapter 3: Design patterns for developing enterprise GenAI applications. Chapter 4: Introduction to Agentic -- Chapter 5: End to end implementation of a practical Use case. Chapter 6: Evaluation and Deployment. PART - IV -- Chapter 7: Responsible AI & Risk framework -- Chapter 8: Conclusion and best practices.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/979-8-8688-1603-1
ISBN:	9798868816031

Enterprise guide for implementing generative AI and agentic AI = a practical guide to developing, deploying, and operationalizing AI-driven applications for enterprise use /
Edward, Shakuntala Gupta.

Enterprise guide for implementing generative AI and agentic AIa practical guide to developing, deploying, and operationalizing AI-driven applications for enterprise use /[electronic resource] :by Shakuntala Gupta Edward, Rahul Bhattacharya, Vikas Sinha. - Berkeley, CA :Apress :2025. - xv, 409 p. :ill., digital ;24 cm.

PART - I -- Chapter 1: Introduction: Evolution of AI and Large language models (LLM). PART - II -- Chapter 2: Generative AI in Business. PART - III -- Chapter 3: Design patterns for developing enterprise GenAI applications. Chapter 4: Introduction to Agentic -- Chapter 5: End to end implementation of a practical Use case. Chapter 6: Evaluation and Deployment. PART - IV -- Chapter 7: Responsible AI & Risk framework -- Chapter 8: Conclusion and best practices.

Generative AI is a growing trend, and its impact is profound and widespread across industries. Organizations are increasingly using it to drive innovations and enhance their problem-solving capabilities. While proof-of-concepts (POCs) showcase the potential of this technology in driving creative advancements, the latest trend shows a movement from POCs to hardened and responsible production implementation of the technology. The technology is not only empowering organisations but also laying the foundation for the next-gen users, enabling them to co-work with the technology. This book begins with a thorough introduction to artificial intelligence, tracing its development from early machine learning models to the sophisticated large language models (LLMs) of today. Next, it emphasizes how AI transforms industries by covering possible use cases across business functions. It covers the role of LLMs as a decision-makers, demonstrating their potential to go beyond being mere assistants. The book covers Gen AI development and deployment methodologies for enterprises. It introduces the readers to the importance of following MLOps, LLMOps, and responsible AI principles while implementing Gen AI solutions for an enterprise. It is the implementation of these principles which expedites the movement of the solution from the POC stage to the production stage. Finally, the book concludes with a summary of key insights, best practices for deploying and scaling generative AI within enterprises, and a glimpse into future trends and recommendations for staying ahead in the AI-driven business landscape. You Will: Learn how to develop and implement production ready GenAI use case. Discover best practices for developing an GenAI solutions, which supports scalable, secure, and production-ready deployments. Understand how to assess and mitigate risks associated with AI, focusing on responsible AI principles and frameworks for ensuring ethical and compliant AI solutions.

ISBN: 9798868816031

Standard No.: 10.1007/979-8-8688-1603-1doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335 / .E39 2025

Dewey Class. No.: 006.3

Enterprise guide for implementing generative AI and agentic AI = a practical guide to developing, deploying, and operationalizing AI-driven applications for enterprise use /
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