Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
Dean Wampler

Dean Wampler
Vice President, Fast Data Engineering, Lightbend

Website | @deanwampler

Dean Wampler is the vice president of fast data engineering at Lightbend, where he leads the Lightbend Fast Data Platform project, a distribution of scalable, distributed stream processing tools including Spark, Flink, Kafka, and Akka, with machine learning and management tools. Dean is the author of Programming Scala and Functional Programming for Java Developers and the coauthor of Programming Hive, all from O’Reilly. He’s a contributor to several open source projects. A frequent Strata speaker, he’s also the co-organizer of several conferences around the world and several user groups in Chicago.

Sessions

9:00am–12:30pm Tuesday, March 26, 2019
Hands-on machine learning with Kafka-based streaming pipelines
Data Engineering & Architecture, Streaming and IoT
 Location: 2007
Secondary topics:  Data Integration and Data Pipelines, Data preparation, data governance, and data lineage, Model lifecycle management
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
Average rating: ***..
(3.85, 13 ratings)
Boris Lublinsky and Dean Wampler walk you through using ML in streaming data pipeline and doing periodic model retraining and low-latency scoring in live streams. You'll explore using Kafka as a data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, model metadata tracking, and other techniques. Read more.
3:50pm–4:30pm Thursday, March 28, 2019
Executive Briefing: What it takes to use machine learning in fast data pipelines
Executive Briefing and best practices, Strata Business Summit
 Location: 2020
Secondary topics:  Streaming, realtime analytics, and IoT
Dean Wampler (Lightbend)
Average rating: ****.
(4.33, 6 ratings)
Your team is building machine learning capabilities. Dean Wampler demonstrates how to integrate these capabilities in streaming data pipelines so you can leverage the results quickly and update them as needed and covers challenges such as how to build long-running services that are very reliable and scalable and how to combine a spectrum of very different tools, from data science to operations. Read more.