Things No One Tells You About Testing Machine Learning

How to avoid disaster

You’re ready to deploy your smartly conceived, expertly tuned, and accurately trained algorithm into that final frontier called “production.” You have collected a quality test set and are feeling cheerful about your algorithm’s performance. Time to ship it and call it a good day’s work! Not so fast.

No one wants to be called on after the fact to fix an embarrassingly broken ML application. If your customers have called out that something is amiss, you’ve …

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