Spark for Reactive Machine Learning: Building Intelligent Agents at Scale

Topic: Spark for Reactive Machine Learning: Building Intelligent Agents at Scale

Presenter: Jeff Smith, Data Engineering Lead - http://x.ai

Abstract: If you want to build an intelligent agent, the first thing that you're going to need is a lot of data. Then, the second thing that you're going to need is some way to process all that data. Enter Apache Spark, the biggest thing to happen to big data since Hadoop MapReduce. With Spark, its machine learning libraries, and related projects, you can learn machine learning models over arbitrarily-sized datasets.

This talk will be a technical exploration of different aspects of building a large scale machine learning system like an intelligent agent. The focus will be on using Spark and related tools to build machine learning systems that stay responsive in the face of errors or changes in scale. These sorts of considerations are crucial for any team looking to build an agent who is expected to be always available and always learning. You can build just a truly reactive system to support an intelligent agent. This talk will show you how to do so by harnessing the power of Apache Spark and other important open source libraries.

Keynote Speaker Bio: Jeff Smith builds large-scale machine learning systems using Scala and Spark. For the past decade, he has been working on data science applications at various startups in New York, San Francisco, and Hong Kong. He’s a frequent blogger and the author of “Reactive Machine Learning Systems,” an upcoming book from Manning on how to build real-world machine learning systems using Scala, Akka, and Spark. He leads the data engineering team at http://x.ai , building Amy, an artificial intelligence who schedules meetings.

Agenda:

7:00pm - 7:20pm Introduction by IBM - Apache Spark

7:20pm - 8:00pm Keynote presentation - Spark for Reactive Machine Learning: Building Intelligent Agents at Scale

8:00pm - 8:30pm Networking and Refreshments

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