Chris Fregly explores an often-overlooked area of machine learning and artificial intelligence—the real-time, end-user-facing "serving” layer in hybrid-cloud and on-premises deployment environments. Serving models to end users in real time in a highly scalable, fault-tolerant manner requires an understanding of not only machine learning fundamentals but also distributed systems and scalable microservices. Drawing on his time at both Databricks and Netflix, Chris shares a 100% open source, real-world, hybrid-cloud, on-premises, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly scalable and highly available robustness.
Chris Fregly is a research scientist at PipelineIO, a San Francisco-based streaming machine learning and artificial intelligence startup. Previously, Chris was a distributed systems engineer at Netflix, a data solutions engineer at Databricks, and a founding member of the IBM Spark Technology Center in San Francisco. Chris is a regular speaker at conferences and meetups throughout the world. He’s also an Apache Spark contributor, a Netflix Open Source committer, founder of the Global Advanced Spark and TensorFlow meetup, author of the upcoming book Advanced Spark, and creator of the upcoming O’Reilly video series Deploying and Scaling Distributed TensorFlow in Production.
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