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MLOps at DoorDash

On Demand

Type

  • Session

Format

  • Hybrid

Track

  • Data Science, Machine Learning and MLOps

Industry

  • Retail and Consumer Goods

Difficulty

  • Intermediate

Room

  • Moscone South | Upper Mezzanine | 159

Duration

  • 35 min
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Overview

MLOps is one of the widely discussed topics in the ML practitioner community. Streamlining the ML development and productionalizing ML are important ingredients to realize the power of ML, however it requires a vast and complex infrastructure. The ROI of ML projects will start only when they are in production. The journey to implementing MLOps will be unique to each company. At DoorDash, we’ve been applying MLOps for a couple of years to support a diverse set of ML use cases and to perform large scale predictions at low latency. This session will share our approach to MLOps, as well as some of the learnings and challenges. In addition, it will share some details about the DoorDash ML stack, which consists of a mixture of homegrown solutions, open source solutions and vendor solutions like Databricks.

Session Speakers

Headshot of Hien Luu

Hien Luu

Sr. Engineering Manager

DoorDash

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