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Srikanth Velamakanni

Co-Founder, Group Chief Executive & Executive Vice-Chairman

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Srikanth is the Co-founder and Executive Vice Chairman of Fractal. Fractal is an AI firm that powers decision-making for some of the world's largest, most admired companies. Srikanth is also a founder & trustee at Plaksha University, where he teaches a course on decision-making. He is a member of the executive council at Nasscom. He serves as an Independent Director at Metro Brands, BARC India & NIIT Ltd., and a Non-Executive Chairman at ideaForge. Srikanth believes in extreme client-centricity and taking a long-term view of business. He considers himself a lifelong student of mathematics and the behavioral sciences.

As India stands on the brink of a technological revolution, the potential for Generative AI (GenAI) to reshape industries and society is immense. This fire-side chat brings together two visionary leaders who have been instrumental in the country’s technological and educational advancements: Srikanth Velamakkani, Co-founder, Group Chief Executive & Vice-Chairman of Fractal, and Rajendra Singh Pawar, Chairman & Founder of NIIT Group. The panel will be moderated by Kunal Jain, Founder & CEO - Analytics Vidhya.

The discussion aims to identify the key levers and initiatives needed for building the next-generation GenAI ecosystem in India. We would discuss the diverse thoughts and perspectives form the leaders in front of the community.

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In an era where artificial intelligence is transforming industries and redefining the way we work, decision-making processes are undergoing a profound shift. Join us for a thought-provoking keynote session on "Decision Making in the Age of AI," where we will explore the intersection of human intuition and machine intelligence.

 

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

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