Developer Tools · Azure AI Foundry
Automates the full MLOps lifecycle — model evaluation, deployment gating, performance monitoring, and drift detection — using Azure Machine Learning and Azure AI Foundry. Human-in-the-loop only for exceptions.
How It Works
We build Prompt Flow evaluation pipelines that run automatically on every model candidate — scoring against your ground truth dataset and custom quality metrics.
Evaluation results are compared against configurable pass/fail thresholds. Only models that meet all criteria are promoted to production — without manual intervention.
Once in production, the agent monitors model performance continuously — detecting drift, latency spikes, and safety violations, with automatic rollback on severe degradation.
Capabilities
Runs model evaluation pipelines against ground truth datasets automatically on every deployment candidate — no manual eval steps, consistent scoring methodology.
Enforces quality thresholds before any model reaches production. If a candidate fails evaluation benchmarks, deployment is blocked and the team is notified with specific failure details.
Monitors production model output quality in real time — detecting drift in accuracy, latency, or output coherence and alerting the team before users notice degradation.
Every model deployment is tracked with full lineage: training data snapshot, evaluation results, deployment timestamp, and the engineer who approved it — for audit and rollback.
Automated fairness metrics, toxicity scores, and content safety reports generated for every model deployment — surfaced in Azure AI Foundry's Responsible AI dashboard.
When production degradation is detected, the agent orchestrates an automatic rollback to the last stable model version — with notification to the team and post-mortem ticket creation.
Built With