Short description:

Artificial Intelligence (AI) is the bedrock of today's applications, propelling the field towards Artificial General Intelligence (AGI). Despite this advancement, integrating such breakthroughs into large-scale production-grade enterprise applications presents significant challenges. This book addresses these hurdles in the domain of large language models within enterprise solutions.

By leveraging Big Data engineering and popular data cataloguing tools, you?ll see how to transform challenges into opportunities, emphasizing data reuse for multiple AI models across diverse domains. You?ll gain insights into large language model behavior by using tools such as LangChain and LLamaIndex to segment vast datasets intelligently. Practical considerations take precedence, guiding you on effective AI Governance and data security, especially in data-sensitive industries like banking.

This enterprise-focused book takes a pragmatic approach, ensuring large language models align with broader enterprise goals. From data gathering to deployment, it emphasizes the use of low code AI workflow tools for efficiency. Addressing the challenges of handling large volumes of data, the book provides insights into constructing robust Big Data pipelines tailored for Generative AI applications. Scaling Enterprise Solutions with Large Language Models will lead you through the Generative AI application lifecycle and provide the practical knowledge to deploy efficient Generative AI solutions for your business.

Long description:

Artificial Intelligence (AI) is the bedrock of today's applications, propelling the field towards Artificial General Intelligence (AGI). Despite this advancement, integrating such breakthroughs into large-scale production-grade enterprise applications presents significant challenges. This book addresses these hurdles in the domain of large language models within enterprise solutions.

By leveraging Big Data engineering and popular data cataloguing tools, you?ll see how to transform challenges into opportunities, emphasizing data reuse for multiple AI models across diverse domains. You?ll gain insights into large language model behavior by using tools such as LangChain and LLamaIndex to segment vast datasets intelligently. Practical considerations take precedence, guiding you on effective AI Governance and data security, especially in data-sensitive industries like banking.

This enterprise-focused book takes a pragmatic approach, ensuring large language models align with broader enterprise goals. From data gathering to deployment, it emphasizes the use of low code AI workflow tools for efficiency. Addressing the challenges of handling large volumes of data, the book provides insights into constructing robust Big Data pipelines tailored for Generative AI applications. Scaling Enterprise Solutions with Large Language Models will lead you through the Generative AI application lifecycle and provide the practical knowledge to deploy efficient Generative AI solutions for your business.

What You Will Learn

• Examine the various phases of an AI Enterprise Applications implementation.


• Turn from AI engineer or Data Science to an Intelligent Enterprise Architect.


• Explore the seamless integration of AI in Big Data Pipelines.


• Manage pivotal elements surrounding model development, ensuring a comprehensive understanding of the complete application lifecycle.


• Plan and implement end-to-end large-scale enterprise AI applications with confidence.

Who This Book Is For

Enterprise Architects, Technical Architects, Project Managers and Senior Developers.

Table of Contents:

Chapter 1_Machine Learning Primer.- Chapter 2_Natural Language Processing Primer.- Chapter_3: RNN to Transformer and BERT.- Chapter_4: Large Language Models.- Chapter_5: Retrieval Augmented Generation.- Chapter_6: LLM Evaluation and Optimization.- Chapter_7: AI Governance and Responsible AI.- Chapter_8: Adding Intelligence to a Large Enterprise Applications.- Chapter_9: Data Pipelines in Generative AI.- Chapter_10: Putting it all Together.