Tips for Building Practical Machine learning systems

Reading time
1 min read
The Great Wave off Kanagawa by Hokusai

Dear friends,

In an earlier letter, I wrote about the challenge of robustness: A learning algorithm that performs well on test data often doesn’t work well in a practical production environment because the real world turns out to be different than the test set.

Amid the Covid-19 pandemic, many machine learning teams have seen this firsthand:

  • Financial anti-fraud systems broke because consumers changed their behavior. For example, credit card companies often flag a card as possibly stolen if the purchase pattern associated with it suddenly changes. But this rule of thumb doesn’t work well when huge swaths of society start working from home and stop going to restaurants and malls.
  • Logistics models used to predict supply and demand broke when manufacturers, shippers, and consumers changed their behavior. Trained on last year’s data, a model that predicts 1,000 widgets arriving on time next month can’t be trusted anymore.
  • Online services receiving a new surge or plunge in users are rethinking their demand estimation models, since earlier models no longer are accurate.

Although the tsunami of Covid-19 — with its devastating impact on lives and livelihoods — is a dramatic example of change in the world, small parts of the world experience waves of change all the time. A new online competitor may mean that a retail store’s demand estimation model no longer works. A new tariff by a small country subtly shifts supply chain behavior among larger ones.

Building practical machine learning systems almost always requires going beyond achieving high performance on a static test set (which, unfortunately, is what we are very good at). You may need to build an alert system to flag changes, use human-in-the-loop deployments to acquire new labels, assemble a robust MLOps team, and so on.

Technological improvements will make our algorithms more robust to the world’s ongoing changes. For the foreseeable future, though, I expect deploying ML systems — and bridging proof of concept and production deployments — to be rewarding but also hard.

I hope all of you continue to stay safe.

Keep learning!



Subscribe to The Batch

Stay updated with weekly AI News and Insights delivered to your inbox