Akshay R. Kulkarni is an artificial intelligence (AI) and machine learning (ML) evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is a Google developer, an author, and a regular speaker at major AI and data science conferences (including the O'Reilly Strata Data & AI Conference and Great International Developer Summit (GIDS)) . He is a visiting faculty member at some of the top graduate institutes in India. In 2019, he was featured as one of India's "top 40 under 40" data scientists. In his spare time, Akshay enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.

Adarsha Shivananda is a data science and MLOps leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside organizations to solve problems through training programs. He always wants to stay ahead of the curve. Adarsha has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.

Avinash Manure is a seasoned Machine Learning Professional with 10+ years of experience building, deploying, and maintaining state-of-the-art machine learning solutions across different industries. He has 6+ years of experience leading and mentoring high-performance teams in developing ML systems catering to different business requirements. He is proficient in deploying complex machine learning and statistical modeling algorithms/techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.

Explains concepts and applications of prescriptive AI with algorithms that are currently used in the industry

Covers interpreting results from different methodologies

Explains how to integrate decision intelligence frameworks using practical examples

Chapter 1: Decision Intelligence Overview.- Chapter 2: Decision Intelligence Requirements.- Chapter 3: Decision Intelligence Methodologies.- Chapter 4: Interpreting Results from Different Methodologies.- Chapter 5: Augmenting Decision Intelligence Results into the Business Workflow.- Chapter 6: Actions, Biases and Human-in-the-Loop.- Chapter 7: Case Studies.