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.

Think of self-driving cars. Teams of engineers and scientists at companies like Tesla and Google have worked for years to train models for lane maintenance and collision avoidance using a broad array of machine learning techniques. Ultimately, though, engineers deploy those models into thousands of cars that can then react to real-time conditions on the road. Without that deployment step, the extensive efforts of those engineers and scientists would have little real-world value.

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