Sep 23–26, 2019

Dean Wampler
Head of Developer Relations, Anyscale

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

1:30pm–5:00pm Tuesday, September 24, 2019

Location: 1E 15/16

Hands-on machine learning with Kafka-based streaming pipelines

Data Engineering and Architecture

Secondary topics: Model Development, Governance, Operations

Boris Lublinsky (Lightbend), Dean Wampler (Anyscale)

Boris Lublinsky and Dean Wampler examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more. Read more.

4:35pm–5:15pm Thursday, September 26, 2019

Location: 1E 10/11

Executive Briefing: What it takes to use machine learning in fast data pipelines

Executive Briefing and best practices, Strata Business Summit

Secondary topics: Data, Analytics, and AI Architecture, Streaming and IoT

Dean Wampler (Anyscale)

Dean Wampler dives into how (and why) to integrate ML into production streaming data pipelines and to serve results quickly; how to bridge data science and production environments with different tools, techniques, and requirements; how to build reliable and scalable long-running services; and how to update ML models without downtime. Read more.