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

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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.

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