Speaker: Sahil Dua
Senior Software Engineer, Machine Learning @Google, Stanford AI, Co-Author of “The Kubernetes Workshop”, Open-Source Enthusiast
Sahil Dua is a Tech Lead focused on developing and adapting large language models (LLMs) with an expertise in Representation Learning. He oversees the full LLM lifecycle, from designing data pipelines and model architectures to optimizing models for highly efficient serving. Before Google, Sahil worked on the ML platform at Booking.com to scale machine learning model development and deployment.
A co-author of “The Kubernetes Workshop” book and an open-source enthusiast, Sahil has contributed to projects like Git, Pandas, and Linguist. As a frequent speaker at global conferences, he shares insights on AI, machine learning, and tech innovation, inspiring professionals across the industry.
Session
Building Embedding Models for Large-Scale Real-World Applications
Embedding models are at the core of search, recommendation, and retrieval-augmented generation (RAG) systems, transforming data into meaningful representations.