The 2017 machine learning outlook
Drew Paroski and Gary Orenstein on the rapid spread of machine learning and predictive analytics
Tower viewer. (source: Pexels)
Machine learning has been a mainstream commercial field for some time now, but it’s going through an important acceleration. In this podcast episode, I talk about that acceleration with two executives from MemSQL, a company that specializes in in-memory databases: Gary Orenstein, MemSQL chief marketing officer, and Drew Paroski, MemSQL vice president of engineering.
Orenstein and Paroski identify a few crucial inflections in the machine learning landscape: machine learning models have become easier to write; computing capacity on the cloud has increased dramatically; and new sources of data—everything from drones to smart-home devices and industrial controllers—have added new richness to machine learning models.
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Computing capacity and software progress have made it possible to train some machine learning models in real time, says Orenstein: “given enough time in computing, you can do just about anything, but only recently have people been able to apply these machine learning models in real time to critical business processes.”
Other discussion topics:
- How the machine learning field overlaps with the data science field
- How energy companies use real-time predictive analytics to operate wind farms and oil fields
- What skills managers need to consider as they’re building teams that specialize in machine learning
- How to build a real-time data pipeline, and how to consider cloud versus on-premise infrastructure
This post and podcast are part of a collaboration between MemSQL and O’Reilly. See our statement of editorial independence.