Machine learning can provide companies with a competitive advantage by using the data they’re collecting — for example, purchasing patterns — to generate predictions that power revenue-generating products (e.g. e-commerce recommendations). But it’s difficult for any one employee to keep up with — much less manage — the massive volumes of data being created. That poses a problem, given AI systems tend to deliver superior predictions when they’re provided up-to-the-minute data. Systems that aren’t regularly retrained on new data run the risk of becoming “stale” and less accurate over time.
Fortunately, an emerging set of practices dubbed “MLOps” promises to simplify the process of feeding d …
