Driving Change: The Advantages of Streaming Architecture

There is a massive shift underway, a change in the best way to build things, and a key aspect of that change is a widespread and fundamental use of streaming architecture. The advantages of an event-stream-based approach go far beyond just the need to address real-time or low latency applications at huge scale, although that’s a big part of why people work with streaming data. Beyond low latency, additional drivers include the need for flexibility and agility, so a streaming microservices style is rapidly gaining in adoption. In the first part of this talk we will examine the advantages and issues involved in working with streaming data, take a look at emerging technologies for streaming and describe best practices for this style of work, using use world examples from a variety of industries such as telecommunications, manufacturing and web-based services. From there, we will move into a more advanced example from machine learning. One of the common surprises that practitioners face in machine learning is that the logistical difficulties of fielding a machine learning system - such as managing multiple models through evaluation and into production - typically outweigh the actual machine learning itself. The audience will learn how streaming architecture can substantially simplify those logistics, including:

- How to design a rendezvous server to make model deployment easier and more flexible
- How to build a more reliable machine learning system that meets service-level agreements
- How to do better model-to-model comparisons
- How to integrate data engineers and ops into a machine learning team. This presentation will be self-contained and will not require any particular knowledge of streaming architecture or machine learning. We assume that attendees have a basic knowledge of software and familiarity with basic software engineering will be helpful. While accessible, this talk should also be useful for more advanced practitioners of the trade.

Speakers: Dr. Ellen Friedman and Dr. Ted Dunning, MapR Technologies

Monday, 03/26/18


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