Seattle, WA
December 10–13, 2018
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Tuesday, December 11 • 2:35pm - 3:10pm
Machine Learning Model Serving and Pipeline Using KNative - Animesh Singh & Tommy Li, IBM

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Lifecycle support— including continuous development, training, testing, and deployment of machine learning models—and continuous integration (CI) for AI applications is still in its infancy. We need a solution that enables end-to-end automation of data preparation and model deployment pipelines.

In this talk we are going to show how to leverage KNative components to create an event driven AI pipeline. We will leverage OpenWhisk and Kubernetes to provide an event driven platform, and Istio for traffic management and observability to construct a pipeline which will provide interfaces to various open source tools: model training, validation. serving platforms on Kubernetes

We will show how we can leverage this AI pipeline to train using advanced batch scheduling in Kubernetes, automate A/B tests and canary testing of models, monitoring concept drifts and accuracy losses etc.

avatar for Tommy Li

Tommy Li

Senior Software Engineer, IBM
Tommy Li is a software developer in IBM focusing on Cloud, Kubernetes, and Machine Learning. He is one of the Kubeflow committers and worked on various open-source projects related to Kubernetes, Microservice, and deep learning applications to provide advanced use cases on cloud-computing... Read More →
avatar for Animesh Singh

Animesh Singh

Chief Architect and Program Director, IBM
Animesh Singh is a Program Director and Chief Architect for the IBM Watson and Cloud Open Source Platform, where he leads machine learning and deep learning initiatives on IBM Cloud and works with communities and customers to design and implement deep learning, machine learning, and... Read More →

Tuesday December 11, 2018 2:35pm - 3:10pm PST