The best way to start and scale ML production monitoring

Nobody can possibly know your models as well as you, their creator. So when it comes to configuring monitors for ML production, you’re the natural choice.

Aporia provides a rich variety of monitoring options and modules to help you to build the most suitable models and optimize them for your cases and data.

ML production observability starts here

Visibility into Actual Performance

Models rarely, if ever, perform in real life with the same levels of precision, recall and accuracy that they achieve during the research phase. But how can you be sure when reasonable deviation tips into poor fit?
The Aporia dashboard provides a single pane of glass for checking ML production status, continuously comparing actual performance with expected results. Customize views to track the measures that matter most for each model – average prediction, version comparison, or sub-segment performance.

Rich Monitoring Options

Aporia proactively monitors your models to ensure early detection of the full range of issues. From type mismatch to prediction drift, and from model staleness to custom metric degradation and more, Aporia helps you cover all monitoring bases.

Complementary Investigation Toolbox

Once an issue has been detected, its extent and causes must be assessed to determine the right action to take. From the alert, dive right into Aporia’s quick triage zone, where you’ll find essential diagnostic information.

Get quick answers about which elements of your data have been affected; whether the issue impacts the full model or just a segment if the issue revolves around specific features or timeframes, and much more.

Aporia helps you identify the source of the problem at hand – data integrity, data science, or engineering – so you can expedite root cause analysis and remediation.