We’ve gotten pretty good at building machine learning models. From legacy platforms like SAS to modern MPP databases and Hadoop clusters, if you want to train up regression or classification models, you're in great shape. In contrast, deploying those models is a face-meltingly painful experience. This despite the fact that machine learning models are primarily only useful to a business insofar as they're deployed into operational systems that influence the business’ behavior.
In an effort to demystify machine learning, Josh will cover what he believes is the clear path forward: an approach to machine learning deployment based on an industry standard, language-agnostic, and ability to represent a broad range of algorithms. And it’s urgent — if the work of data scientists never gets deployed into operational processes, it will deliver little value and reinforce the nascent sense of disillusionment in the market concerning data science and machine learning.
