May 12, 2021

Deploying models for practical use is an industrial concern that generally goes unaddressed in research. As a result, publications on the subject tend to come from the major AI companies.

Craig Wiley has journeyed from the hand-deployed models of yore to the pinnacle of automated AI. Today, as chief product manager of Google Cloud’s AI services, he’s making advanced tools and processes available to anyone with a credit card.

So you’ve trained an accurate neural network model in a Jupyter notebook. You should celebrate! But . . . now what? Machine learning engineering in production is an emerging discipline that helps individual engineers and teams put models into the hands of users.

Foiling attackers who try to lure email users into clicking on a malicious link is a cat-and-mouse game, as phishing tactics evolve to evade detection. But machine learning models designed to recognize phishing attempts can evolve, too, through automatic retraining and checks to maintain accuracy.

A computer vision model, continually trained and automatically updated, can boost quality control in factories. Landing AI, a machine learning platform company led by Andrew Ng, helped a maker of compressors for refrigeration check them for leaks.

When every email, text, or call a company receives could mean a sale, reps need to figure out who to reply to first. Machine learning can help, but using it at scale requires a highly automated operation.