Many people seem to be under the impression that machine learning is like a perpetual motion machine: once you get it started, it can run forever on its own without further input. But while this image makes for entertaining science fiction, it doesn’t come close to matching the reality of AI. (As a side note, I place blame for this misconception largely on Hollywood.) The truth is that AI maintenance is a critical piece to any successful data science program.
Training a model on a pre-curated dataset is easy; scaling across your company and dealing with changing requirements is much harder.
An AI System is Like a Car’s Engine
Don’t think of AI as a perpetual motion machine. Instead, think of it like a car’s engine. At the start, it runs fast and well. But over time, all the moving parts wear down and fail.
In AI’s case, the moving parts are the data you feed it, the machine learning algorithms and neural networks it runs, and the ecosystem it operates in.
Without regular oil changes and other upkeep, an internal combustion engine will quickly fall apart. And like a car, if you don’t keep up with regular maintenance on your AI, it’ll end up parked on the lawn with a “For Sale, You Tow” sign in the windshield.
Understand AI Maintenance Best Practices
Maintaining your AI can be as easy as replacing the cabin air filter, or as complicated as rebuilding the transmission. That’s why it’s important to understand some basic AI maintenance best practices.
Never forget that without a data science program to use it, a trained model is all dressed up with nowhere to go. Data science is more than model-building: it’s also gathering data, cleaning and annotating it, and re-training the model. And to keep your model up-to-date, you need to do that over and over again. Without an established methodology, your model will quickly break down.
Training a model on a pre-curated dataset is easy; scaling across your company and dealing with changing requirements is much harder. My experience shows that if you don’t have a good pipeline for sampling and annotating this data and managing the complexity as it grows over time, you’ll quickly run into problems.
In this guest post for KDNuggets.com, I break down the steps you need to take in order to keep your AI in tip-top shape. By planning for problems, ensuring you respond to them quickly, and managing for changes in the industry, you can keep your AI firing on all cylinders.