Mar 15–18, 2020
Dean Wampler

Dean Wampler
Head of Developer Relations, Anyscale

Website | @deanwampler

Dean Wampler is an expert in streaming data systems, focusing on applications of machine learning and artificial intelligence (ML/AI). He’s head of developer relations at Anyscale, which is developing Ray for distributed Python, primarily for ML/AI. Previously, he was an engineering VP at Lightbend, where he led the development of Lightbend CloudFlow, an integrated system for building and running streaming data applications with Akka Streams, Apache Spark, Apache Flink, and Apache Kafka. Dean is the author of Fast Data Architectures for Streaming Applications, Programming Scala, and Functional Programming for Java Developers, and he’s the coauthor of Programming Hive, all from O’Reilly. He’s a contributor to several open source projects. A frequent conference speaker and tutorial teacher, he’s also the co-organizer of several conferences around the world and several user groups in Chicago. He earned his PhD in physics from the University of Washington.

Sessions

1:30pm5:00pm Monday, March 16, 2020
Location: LL21 C
Boris Lublinsky (Lightbend), Dean Wampler (Anyscale)
Machine learning (ML) models are data, which means they require the same data governance considerations as the rest of your data. Boris Lublinsky and Dean Wampler outline metadata management for model serving and explain what information about running systems you need and why it's important. You'll also learn how Apache Atlas can be used for storing and managing this information. Read more.
11:00am11:40am Tuesday, March 17, 2020
Location: LL20C
Dean Wampler (Anyscale), Boris Lublinsky (Lightbend)
Production deployment of machine learning (ML) models requires data governance because models are data. Dean Wampler and Boris Lublinsky justify that claim and explore its implications and techniques for satisfying the requirements. Using motivating examples, you'll explore reproducibility, security, traceability, and auditing, plus some unique characteristics of models in production settings. Read more.

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