Executive Briefing: What it takes to use machine learning in fast data pipelines
Who is this presentation for?
- Business executives and managers who want to understand the importance and implications of combining ML, AI, and streaming data pipelines
Dean Wampler helps you develop a conceptual understanding of the challenges faced by your teams as they develop and deploy machine learning (ML) and artificial intelligence (AI) services integrated with fast data (streaming) pipelines. While combining these technologies is challenging, the benefits include timely delivery of innovative services to your customers.
You’ll gain a brief overview of the business justification for integrating ML and AI and streaming as well as the ML and AI scenarios that are best delivered through streaming. Dean walks you through the main challenges when using these technologies together; ways to bridge the gap between data science and production teams, their tools, methods, and sometimes conflicting goals, for example, the exploration of ideas and optimal scoring results versus production reliability and efficiency; streaming ML and AI services must run reliably and handle variable loads for a long time, requiring you to leverage best practices from the microservices world; and updating models in the streaming application before they become stale without downtime and other practical problems.
- Familiarity with ML, AI, and streaming ideas (useful but not required)
What you'll learn
- Understand the business motivations for serving ML and AI in streaming pipelines, the organizational and technical challenges of combining data science and production-hardened, streaming pipelines, and approaches to several specific issues, such as updating models in running pipelines without downtime
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.
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