High Performance TensorFlow and Spark, Deploy Models from Notebook to Production

Sutton CENTRAL

Part of the O'Reilly AI NYC Conference!

ATTENDANCE IS FREE AND OPEN TO THE PUBLIC!

YOU DO NOT NEED A CONFERENCE TICKET TO ATTEND!!

Where:

New York Hilton Midtown, Sutton CENTRAL
1335 Avenue of the Americas
New York, NY

Description

In this completely 100% Open Source demo-based talk, Chris Fregly from PipelineIO will be addressing an area of machine learning and artificial intelligence that is often overlooked: the real-time, end-user-facing "serving” layer in a hybrid-cloud and on-premise deployment environment using Jupyter, NetflixOSS, Docker, and Kubernetes.

Serving models to end-users in real-time in a highly-scalable, fault-tolerant manner requires not only an understanding of machine learning fundamentals, but also an understanding of distributed systems and scalable microservices.

Chris will combine his work experience from both Databricks and Netflix to present a 100% open source, real-world, hybrid-cloud, on-premise, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly-scalable and highly-available robustness.

Speaker Bio

Chris Fregly (https://linkedin.com/in/cfregly) is Founder and Research Engineer at PipelineIO (http://pipeline.io/), 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 (https://www.safaribooksonline.com/live-training/courses/high-performance-tensorflow-in-production/0636920082859/) titled, "High Performance TensorFlow in Production (https://www.safaribooksonline.com/live-training/courses/high-performance-tensorflow-in-production/0636920082859/)."

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.