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October 12, 2021
Los Angeles, California + Virtual
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IMPORTANT NOTE: Timing of sessions and room locations are subject to change through Monday, September 13 due to schedule changes that will be made as speakers finalize whether speaking in person or virtually.
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Tuesday, October 12 • 2:05pm - 2:35pm
Serving Machine Learning Models at Scale Using KServe - Yuzhui Liu, Bloomberg

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KServe (previously known as KFServing) is a serverless open source solution to serve machine learning models. With machine learning becoming more widely adopted in organizations, the trend is to deploy larger numbers of models. Plus, there is an increasing need to serve models using GPUs. As GPUs are expensive, engineers are seeking ways to serve multiple models with one GPU. The KServe community designed a Multi-Model Serving solution to scale the number of models that can be served in a Kubernetes cluster. By sharing the serving container that is enabled to host multiple models, Multi-Model Serving addresses three limitations that the current ‘one model, one service’ paradigm encounters: 1) Compute resources (including the cost for public cloud), 2) Maximum number of pods, 3) Maximum number of IP addresses. 4) Maximum number of services This talk will present the design of Multi-Model Serving, describe how to use it to serve models for different frameworks, and share benchmark stats that demonstrate its scalability.

Speakers
avatar for Yuzhui Liu

Yuzhui Liu

Senior Software Engineer, Bloomberg
Yuzhui Liu is a senior software engineer who works for the Data Science Platform team at Bloomberg. She is passionate about building infrastructure for machine learning applications. In her spare time, she likes to travel around the world and enjoy local food.


Tuesday October 12, 2021 2:05pm - 2:35pm PDT
Room 502 AB + Online