fbpx

Sudeep Das

Head of Machine Learning
DoorDash Inc.

Presentation Title:

Unleashing the Power of Large Language Models at DoorDash for a Seamless Shopping Adventure

Presentation Summary:

Atom icon for The AI Conference 2023, a groundbreaking two-day event on AGI, LLMs, Infrastructure, Alignment, AI Startups, and Neural Architectures.At DoorDash, our expansion into various services like groceries, convenience, and retail goes hand in hand with our commitment to advanced machine learning, especially the integration of Large Language Models (LLMs). In our presentation, we’ll highlight how LLMs are revolutionizing our approach to personalization, search optimization, and product knowledge graph building. These models are instrumental in managing complex scenarios, such as substituting low-in-stock items, and are pivotal in refining our product catalog and taxonomy.

Brain icon for The AI Conference 2023, a groundbreaking two-day event on AGI, LLMs, Infrastructure, Alignment, AI Startups, and Neural Architectures.

We’ll provide an in-depth look at how LLMs, alongside traditional machine learning techniques, are enhancing user experience. This includes facilitating rapid basket building, simplifying the discovery of new products, and ensuring seamless order fulfillment. Our discussion will focus on the significant impact of LLMs, detailing the lessons learned, challenges encountered, and the practical implications of applying these state-of-the-art technologies in real-world applications at DoorDash.

About | Sudeep Das

Sudeep Das is the Head of Machine Learning, New Business Verticals, at DoorDash, leading Personalization, Search, Product Catalog, and Fulfillment related ML applications within the New Verticals. He was previously a Machine Learning Lead at Netflix, where his main focus was on developing the next generation of machine learning algorithms to drive the personalization, discovery and search experience in the product. Sudeep has had more than twenty years of experience in machine learning applied to both large scale scientific problems, as well as in the industry. He is a frequent speaker at RecSys, SIGIR, ICML, ReWork, MLConf, Nordic Media Conference, and other machine learning conferences. He holds a PhD in Astrophysics from Princeton University.