Complications such as data drift, data anomalies and data errors will inevitably appear once a model is deployed to production. AI Continuous Testing answers both the what and the why of changing data. It not only detects issues as they happen but also provides insight into their root causes - shortening the time to resolution.
Production data is noisy, and model performance can decline over time. Discovering the underlying driver of performance declines is a time-consuming process. AI Continuous Testing automatically surfaces the specific issue and the associated data points that are to blame.
When you have multiple models in production, maintaining oversight over their behavior can be a manual and time-consuming process. AI Continuous Testing automatically tracks model performance across all of your production models so that you can focus on remediating issues that are surfaced.
Data is inherently contextual and thus requires analysis to establish thresholds for production validity. AI Continuous Testing automates this thresholding process and generates alerts when such thresholds are crossed.
Go beyond simple performance monitoring for your complex models. The RI platform provides native and rich monitoring of NLP, CV, and tabular models.
Deploy in a single line of code. Connect to your production ML framework, with hooks into your alerting and retraining pipeline of choice.
Spend less time fiddling around with monitoring dashboards and more time fixing the issue with automatic root cause analysis of model issues in production.
Define and track the metrics that matter for your business, in addition to the metrics that come ready out of the box.