Large Language Models Ops for Finance

Explore emerging technologies and the evolving role of AI in finance. Geared toward finance professionals, this book will equip you with the knowledge and tools to harness the power of Large Language Models (LLMs), ensuring you stay ahead in an increasingly AI-driven industry.

Highlighting the benefits and challenges of LLMs in financial contexts, the book starts with the necessary infrastructure setup, covering both hardware and software requirements. It offers a balanced discussion on cloud versus on-premises solutions, enabling you to make informed decisions based on their specific needs. Training and fine-tuning LLMs are critical components of effective deployment, and this book offers best practices, from data preparation to advanced fine-tuning techniques. It also delves into deployment strategies, with practical advice on building deployment pipelines, monitoring performance, and optimizing operations.

Ensuring data privacy and security is paramount in finance, so you’ll take a close look at maintaining compliance with regulations while safeguarding sensitive information. You’ll also examine the integration of LLMs into existing financial systems, with real-world case studies and strategies for API development and real-time data processing. Monitoring and maintenance are crucial for long-term success, and the book outlines how to manage performance metrics, handle model drift, and ensure regular updates. Large Language Models Ops for Finance is your essential guide to discovering the transformative potential of LLMs in the finance industry.  

What You Will Learn

●        Review LLMs and their applications in finance.

●        Set up the infrastructure for training and deploying LLMs.

●        Apply best practices for fine-tuning and maintaining LLMs.

●        Employ techniques for integrating LLMs into existing financial systems

Who This Book Is For

AI and ML engineers, data scientists, and finance professionals interested in implementing and managing large language models within the finance industry.

Brindha Priyadarshini Jeyaraman is a seasoned AI and Data Science leader with over 16 years of experience spanning machine learning, software engineering, and cloud architecture. As Principal Architect for AI, APAC at Google Cloud, she leads AI-driven transformations across diverse sectors including telecommunications, finance, gaming, and AI agents for the consumer ecosystem. An expert in real-time systems, MLOps, and Temporal Knowledge Graphs, Brindha has authored three influential books on machine learning, streaming analytics, and financial observability. Her technical leadership has significantly advanced AI adoption and deployment practices across the APAC region, strengthening partner ecosystems and shaping enterprise AI strategies.

She holds a Master’s in Knowledge Engineering from the National University of Singapore and a Doctor of Engineering in AI with a specialization in Temporal Knowledge Graphs in Finance from Singapore Management University. Brindha is deeply passionate about mentoring the next generation of AI professionals and championing diversity and inclusion in the tech industry. Brindha is recognized for her innovative approach to solving complex problems and is a leading voice in the AI community.