Enterprise Guide for Implementing Generative AI and Agentic AI

Rahul Bhattacharya is a thought leader, consultant and an active speaker in AI & ML. In his current role as the AI leader of a global firm, he is responsible for working with stakeholders across globe to drive transformative AI programs for clients across industries and functions. With over 30 years of diverse experience in implementing real-world business applications using AI and machine learning globally, Rahul is a trusted advisor and mentor in his field.  Rahul is a Birla Institute of Technology and Science (BITS), Pilani graduate and is passionate about learning and teaching. He defines himself as a curious traveler, consummate foodie and lifelong objectivist. A student of global cultures, he has an eclectic taste in literature, music and movies. He is a husband and a father of two Gen-Z boys.Shakuntala Gupta is a thought leader, consultant in the areas of AI, ML, NLP, Big Data Analytics, product development. In her current role as the AI and Data leader she helps deliver transformative AI programs for clients across various industries and functions.  With over two decades of diverse experience in implementing advanced analytics solutions for real-world business challenges using AI, Data and machine learning, Shakuntala is a true technology enthusiast. With a master’s degree in computer science, she defines herself as a continuous learner. Outside of her technical work she loves exploring new cuisines, indulges herself in fiction books and cherish her time at beach whenever possible.Vikas Sinha is a Machine Learning Engineer, Architect and a thought leader in AI & ML. As organizations struggles to scale AI and realize business impact, his role is to conceptualize, build and deploy AI, ML @scale for Clients across sectors globally working with client enterprise and data architects, CDOs and Businesses. Vikas has expertise across cloud platforms and have developed end to end Responsible MLOps, LLMOps architectures on Azure, AWS and GCP enabling clients to productionize multiple AI use cases. He has also led innovations in client engagements through implementation of novel research in multi-modal AI, simplified AI, and enabling operationalization of complex AI architecture as part of enterprise solution. He also mentors’ college graduates in the field of Data Science, AI/ML/GenAI/MLOps/LLMOps and have delivered guest lectures at reputed Indian Universities. He is father to a beautiful angel.

• PART – I.- Chapter 1:  Introduction: Evolution of AI and Large language models (LLM). PART – II.- Chapter 2:  Generative AI in Business. PART – III.-  Chapter 3:  Design patterns for developing enterprise GenAI applications. Chapter 4: Introduction to Agentic.- Chapter 5: End to end implementation of a practical Use case. Chapter 6: Evaluation and Deployment. PART – IV.- Chapter 7: Responsible AI & Risk framework.- Chapter 8: Conclusion and best practices.