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Monitor production ML drift and degradation with alerts and retraining guidance.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP ML Monitor" yet — see the docs or source repo.
Monitor the production churn prediction model for input feature drift, prediction confidence changes, and AUC decline. Trigger alerts when any metric exceeds thresholds and recommend whether retraining is needed.
A monitoring report showing drift and performance issues, alert details, and retraining recommendations.
Set up production monitoring for an ecommerce recommendation model. Track CTR, conversion rate, latency, and drift in key features. Output automated alert rules by severity and explain when the model should be rolled back immediately.
A severity-based alerting plan with monitored metrics, thresholds, and rollback triggers.
Analyze the last 30 days of production performance for a fraud detection model. Use precision, recall, false positive rate, and data drift signals to decide whether retraining should start, and suggest priority features and data windows.
A retraining decision recommendation with rationale, priority features, and suggested training data windows.
Monitor and manage Modal training jobs for long-running GPU workloads.
Filter Kubernetes warning events so AI can diagnose cluster issues faster.
Track AI usage, costs, logs, and debug model interactions across apps.
Scan codebases for incompleteness and quantify LLM context drift risk.
Monitor system resources, processes, networks, and logs for diagnostics and troubleshooting.
Use safe MCP tools for calculation, search, drift checks, eval comparison, and grading.