Additional information
by Arindam Ganguly (Author)
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.
Back Jacket
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.
Author Biography
Arindam Ganguly is an experienced Data Scientist in one of the leading Multi-National Software Service Firm where he is responsible for developing and designing intelligent solutions leveraging his expertise in Artificial Intelligence and Data Analytics. He has over 8 years of experience delivering enterprise products and applications and has proven skill sets in developing and managing a number of software products with various technical stacks.
Arindam also is well-versed in developing automation and hyper-automation solutions leveraging automated workflow engines and integrating them with AI. Additionally, he is the author of Build and Deploy Machine Learning Solutions using IBM Watson, which teaches how to build artificial intelligent applications using the popular IBM Watson toolkit.
