Who this book is for

The book is designed to mainly appeal to a technical audience with some basic Python code foundations. However, the theoretical chapters and the hands-on exercises are based on generative AI foundations and industry-led use cases, which might be of interest to non-technical audiences as well.

Overall, the book caters to individuals interested in gaining a comprehensive understanding of the transformative power of LLMs and define, enabling them to navigate the rapidly evolving AI landscape with confidence and foresight. All kinds of readers are welcome, but readers who can benefit the most from this book include:

• Software developers and engineers: This book provides practical guidance for developers looking to build applications leveraging LLMs. It covers integrating LLMs into app backends, APIs, architectures, and so on.
• Data scientists: For data scientists interested in deploying LLMs for real-world usage, this book shows how to take models from research to production. It covers model serving, monitoring, and optimization.
• AI/ML engineers: Engineers focused on AI/ML applications can leverage this book to understand how to architect and deploy LLMs as part of intelligent systems and agents.
• Technical founders/CTOs: Startup founders and CTOs can use this book to evaluate if and how LLMs could be used within their apps and products. It provides a technical overview alongside business considerations.
• Students: Graduate students and advanced undergraduates studying AI, ML, natural language processing (NLP), or computer science can learn how LLMs are applied in practice from this book.
• LLM researchers: Researchers working on novel LLM architectures, training techniques, and so on will gain insight into real-world model usage and the associated challenges.