Put AI to Work
April 15-18, 2019
New York, NY

Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues

Jeremy Lewi (Google), Hamel Husain (GitHub)
1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
Secondary topics:  Platforms and infrastructure, Text, Language, and Speech

Who is this presentation for?

  • ML engineers, data scientists, and DevOps engineers

Level

Intermediate

Prerequisite knowledge

  • Familiarity with ML and Kubernetes (useful but not required)

What you'll learn

  • See how Kubeflow makes it easy to build and deploy ML products on Kubernetes

Description

Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products.

Photo of Jeremy Lewi

Jeremy Lewi

Google

Jeremy Lewi is a cofounder and lead engineer for the Kubeflow project at Google—an effort to help developers and enterprises deploy and use ML cloud natively everywhere. He’s been building on Kubernetes since its inception, starting with Dataflow and then moving onto Cloud ML Engine and now Kubeflow.

Photo of Hamel Husain

Hamel Husain

GitHub

Hamel Husain is a senior data scientist at GitHub, where he’s focused on creating the next generation of developer tools powered by machine learning. His work involves extensive use of natural language and deep learning techniques to extract features from code and text. Previously, Hamel was a data scientist at Airbnb, where he worked on growth marketing, and at DataRobot, where he helped build automated machine learning tools for data scientists.

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