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Shahebaz Mohammad

Lead Applied Machine Learning Engineer

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Renowned as a Kaggle Grandmaster and a LinkedIn Top ML Voice, Shahebaz is a seasoned Applied Machine Learning Engineer at Snorkel AI. His prestigious career is studded with significant positions at leading firms like DataRobot, SocGen, H2O.ai, and Analytics Vidhya, affirming his relentless pursuit of AI advancement and delivering value to Top US Banks.

Beyond his professional endeavors, Shahebaz is a beacon for the machine learning community, sharing insights that bridge theoretical knowledge with practical application, especially in burgeoning fields like data-centric AI and Generative AI.

Prepare for the ultimate AI showdown at this year’s DataHack Summit! We're bringing together 3 AI experts to tackle one challenging case problem using three of the hottest techniques in the field: Retrieval-Augmented Generation (RAG), Long Context Language Models, Fine-Tuning  and combining these approaches. This is your chance to see these cutting-edge methods go head-to-head and understand their unique strengths and weaknesses.

It offers a rare opportunity to compare and contrast different problem-solving approaches, learn from leading experts, and expand your understanding of how versatile and dynamic the field can be.

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Explore the common pitfalls of GenAI implementation and learn how to avoid them. This session delves into why many GenAI projects fail and offers practical strategies for success. Gain insights into best practices for deploying effective, innovative GenAI systems that enhance business outcomes.

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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

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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
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