Scaling AI and Machine Learning with Containers and Kubernetes

AI is popular and yet faces two big challenges in the industry: 1) self-service and automation 2) Deployment into production. These challenges can be addressed with containers and Kubernetes. They help you build AI-as-a-service with open source tools and Kuberentes. Data Scientists can use the service for data, experimentation and to deliver models into production iteratively with self-service and automation. Using Kubernetes, one is able to run massive machine learning pipelines iteratively in an automated fashion that can be repeated. Come find out how we built how industry practitioners run AI service on Kubernetes, issues they have ran into and best practices.
Who is this presentation for?
Data Science practioners, Platform Owners, DevOps,
Prerequisite knowledge:

What you'll learn?

Tushar Katarki is a senior technology professional with experience in cloud architecture, product management and engineering. He is currently at Red Hat as a product manager for OpenShift with focus on AI/ML. He advocates for the use of containers and Kubernetes for AI/ML workflows. Prior to the current role, Tushar has been a product manager and a developer at Red Hat, Oracle (Sun Microsystems), Polycom, Sycamore Networks and Percona. Tushar has an MBA from Babson College and MS in Computer Science from University at Buffalo.