Sign up for our newsletter and get the latest big data news and analysis.

Deploy Serverless TensorFlow Models using Kubernetes, OpenFaaS, GPUs and PipelineAI

In this video from the Stanford HPC Conference, Chris Fregly from PipelineAI presents: Deploy Serverless TensorFlow Models using Kubernetes, OpenFaaS, GPUs and PipelineAI.

“Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements with TensorFlow, Kubernetes, OpenFaaS, GPUs, and PipelineAI.

In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment. This is the holy grail of data science – rapid and safe experiments of ML / AI models directly in production. Following the famous Netflix Culture that encourages “Freedom and Responsibility”, I use this talk to demonstrate how Data Scientists can use PipelineAI to safely deploy their ML / AI pipelines into production using live data. Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong. Learn to be fast and strong by attending this talk!”

Chris Fregly is Founder & Applied AI Engineer at Pipeline AI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, “High Performance TensorFlow in Production.” Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.

See more talks in the Stanford HPC Conference Video Gallery

Check out our insideHPC Events Calendar

Resource Links: