DataDriftIQ

DataDriftIQ is a web app (with optional lightweight desktop agent) that turns “model monitoring” into an AutoML loop. You connect a production data source (warehouse, feature store, or API logs), pick a target, and the system trains a baseline model, deploys it, then continuously watches for drift, data quality breaks, and performance decay. When it detects meaningful degradation, it automatically proposes retraining with a constrained search space, generates a changelog explaining what shifted, and runs shadow tests before promoting a new version. It also includes guardrails: cost caps, fairness checks, and rollback rules. This is not a magic ‘one-click ML’ product—users still need decent labels and stable pipelines—but it removes the constant firefighting that kills ML projects in small teams. Pricing is per monitored model and data volume.