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Dipanjan Sarkar

Head of Community and Principal AI Scientist

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Deepanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, boasting a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end Data products and pioneering Generative AI upskilling programs. A seasoned mentor, Deepanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. Recognitions include "Top 10 Data Scientists in India, 2020," "40 under 40 Data Scientists, 2021," "Google Developer Expert in Machine Learning, 2019," and "Top 50 AI Thought Leaders, Global AI Hub, 2022," alongside global accolades and a Google Champion Innovator title in Cloud AI\ML, 2022.

By the end of the workshop you will be able to:

  • Understand the intricacies of fine-tuning Large Language Models (LLMs) using techniques such as full-finetuning, parameter-efficient fine-tuning (PEFT), and reinforcement learning (RL).
  • Explore advanced fine-tuning methodologies including LoRA, QLoRA, prompt tuning, and prefix-tuning with pros and cons.
  • Implement fine-tuning techniques using libraries like PEFT and TRL, including methods such as Supervised Fine Tuning (SFT), Reward Modeling, and Proximal Policy Optimization (RLHF).
  • Analyze case studies and ethical considerations related to fine-tuning LLMs, ensuring responsible deployment and mitigating potential risks.
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

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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