Raghav Bali

Raghav Bali

Principal Data Scientist

Delivery Hero

Raghav Bali is a Principal Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 13+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications.

Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored more than 8 books, including the second edition of his well received book Generative AI with Python and Pytorch. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

This workshop is designed to provide a comprehensive overview of LLMs, right from foundational NLP concepts to the latest in this domain. This workshop is aimed at working professionals but covers the required details to help beginners get started. You will gain valuable insights and hands-on experience to learn & adapt concepts to your professional lives.

The list of LLMs/models which will be explored/used during the workshop would be BERT, GPT2, LLaMA3, Gemma, OpenAI models
 

Key Takeaways:

  • Understand the fundamentals of Language Models and Transformer architectures.
  • Gain hands-on experience with LLMs and related concepts such as PEFT, Prompt Engineering, RAGs,  LLM frameworks like DSPY.
  • Explore advanced topics such as Reinforcement Learning from Human Feedback (RLHF) and tool-calling through MCP

Prerequisites:

  • Basics/hands-on experience of working with python
  • Basic understanding of Deep Neural Networks (pytorch preferably)
  • Access to google-colab or similar python environment with GPU access

*Note: These are tentative details and are subject to change.

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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

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