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Nischay Dhankhar

Data Scientist

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Nischay Dhankhar is a data scientist and part of the Kaggle Grandmasters team at H2O.ai. He is the youngest 2X Kaggle Grandmaster and the only Indian Kaggler ever to reach the top 6 in the global competition rankings. He is part of the foundational model team, building and conducting extensive research on large language models. Nischay is a co-author of the H2O Danube 1.8B model, the top-performing LLM, which falls in the sub-2 billion parameter range. He is also a Google Developer Expert in the Kaggle category and has participated in Kaggle competitions since he was a teenager. Last year, he graduated with a Bachelor of Engineering from NUST, Delhi, one of India's prestigious engineering universities.

Kaggle is a popular platform for data scientists to showcase their skills, learn from others, and tackle real-world problems. However, approaching Kaggle competitions can be overwhelming, especially for beginners with limited domain knowledge. In this session, Nischay will provide a comprehensive guide on excelling in various Kaggle competitions.

Nischay will share his extensive experience in participating in and winning Kaggle competitions. He will cover various competition types and discuss each domain's latest techniques and approaches. He will also discuss using large language models in competitions and downstream NLP tasks and how well one could perform using AutoML and no-code platforms. Attendees will gain valuable insights into how to get started with competitions, even with zero domain knowledge, and learn effective strategies for winning these competitions. 

Through practical examples and case studies, Nischay will demonstrate his approach to tackling Kaggle competitions, from the initial Exploratory Data Analysis to the final submission. 

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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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