In-person + Virtual
October 11-15
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Thursday, October 14 • 2:30pm - 3:05pm
Serving Machine Learning Models at Scale Using KServe - Animesh Singh, IBM

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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.

avatar for Animesh Singh

Animesh Singh

Distinguished Engineer and CTO - Watson Data and AI OSS Platform, IBM
Animesh Singh is CTO and Director for IBM Watson Data and AI Open Technology, responsible for Data and AI Open Technology strategy. Creating, designing and implementing IBM’s Data and AI engine for AI and ML platform, leading IBM`s Trusted AI efforts, driving the strategy and execution... Read More →

Thursday October 14, 2021 2:30pm - 3:05pm PDT
Room 502 AB + Online