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Enterprise Generative AI Well Architected Framework & Patterns: An Architect's Real-life Guide to Adopting Generative AI

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  • Дата: 21-04-2024, 07:54
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Название: Enterprise Generative AI Well Architected Framework & Patterns: An Architect's Real-life Guide to Adopting Generative AI in Enterprises at Scale
Автор: Suvoraj Biswas, Moumita Biswas
Издательство: Independently published
Год: 2024
Страниц: 114
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 17.1 MB

Welcome to the "Enterprise Generative AI Well Architected Framework & Patterns" a comprehensive step by step guide designed for Enterprise IT Professionals to explore the cutting-edge world of generative artificial intelligence (AI) systems within the context of enterprise applications.

What you will learn from this book:

1. Insights into Generative AI and the Enterprise Architecture surrounding this ecosystem.
2. Learn the Well-Architected framework for designing and building Enterprise Generative AI solutions.
3. Identify various architectural patterns for Large Language Model (LLM) adoption.
4. Make informed decisions on whether to use Retrieval Augmented Generation (RAG) or fine-tuned LLM for your enterprise use case.
5. Acquire hands-on knowledge of Prompt Engineering, Embedding, and Vector databases.

As most of the businesses continue to embrace AI-driven solutions to streamline processes, drive automation and generate valuable insights, the importance of robust and scalable generative AI architectures becomes increasingly evident. When I decided to explore the Generative AI solutions, as a Solutions Architect I faced a lot of challenges, roadblocks and at the same time learned a lot while exploring many tools and technologies during the initial steps. My everyday learning and challenges pushed me to come up with generic well architected frameworks for my fellow IT professionals to build world class software to delight the Enterprise stakeholders following the existing common LLM integration and adoption pattern.

We have all witnessed how OpenAI has recently reshaped the digital landscape through the introduction of tools like ChatGPT, which gained a substantial user base surpassing all popular social media applications. ChatGPT has been powered by what we call Generative AI which not only has remarkable influence in the consumer sphere but also many Enterprises are adopting to solve many business challenges which previously appeared impossible. Generative AI is a form of Deep Learning system which is able to generate new original contents (texts or digital media - audio or Video or images). It uses the Machine Mearning algorithm and artificial neural networks to recognize the underlying pattern in the training data to predict new original contents without any human intervention or influences.

This book is intended to provide readers with a clear understanding of the fundamental principles, methodologies, and best practices for implementing generative AI in large-scale enterprise environments. Whether you are a seasoned AI practitioner(Architects, Engineers / Engineering Managers or Product Managers) seeking to deepen your knowledge or an enterprise leader (VPs, CXOs, Founders) exploring the potential of Generative AI for your organization, this book offers valuable insights into leveraging the power of generative models effectively and responsibly. We will also discuss the challenges and ethical considerations associated with deploying AI systems in enterprise settings and provide strategies to address these concerns.

You can design and develop your own orchestrator component or utilize any open source framework to do the tasks. The engineering and the architecture team need to make a decision on whether to use low code or no code approach. In this section we will discuss two popular Python based application frameworks which can be used as the orchestrator component to build almost any AI apps at scale - LangChain and LlamaIndex. We will also discuss some no code tools to build enterprise grade AI applications without worrying of managing any code.

LLM Filters is a Python library that, when enabled, performs filtering of harmful data within inbound and outbound content to the LLM engine. This library offers a framework for constructing custom filters and can be seamlessly integrated with external messaging systems such as Kafka or RabbitMQ.

I hope that "Enterprise Generative AI Well-Architected Framework & Patterns" will prove to be a valuable resource on your journey to unlock the potential of generative AI within your organization.

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