The 2017 machine learning outlook

Join Steven Camiña of MemSQL for "Building the Ideal Stack for Machine Learning," where he'll share how to use real-time data for machine learning. 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. 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."