ai-mlops

Model drift detection monitors performance degradation in deployed models by comparing prediction patterns against baseline metrics. This Skill detects drift in 18 seconds and triggers automated retraining pipelines, reducing downtime and maintaining accuracy across batch, online, and streaming workloads.

Automated retraining uses event-driven pipelines that monitor data and model performance, then retrain and redeploy models without manual intervention. This Skill orchestrates end-to-end ML lifecycle automation with sub-50ms latency and 2300+ requests per second throughput for production-scale systems.

ML security governance includes prompt injection defense, privacy protections, and data lineage tracking across your ML pipeline. This Skill embeds auditable security controls and governance features directly into production ML workflows for compliance and risk reduction.

Real-time observability for ML systems uses integrated dashboards and automated alerting to surface data quality issues, model performance anomalies, and security events. This Skill provides observability across data ingestion, deployment, and inference stages with incident response automation.

Production ML operations must support batch, online, and streaming workloads with consistent monitoring and governance. This Skill applies unified drift detection, automated retraining, and security controls across all three workload types in a single event-driven pipeline.

Production ML systems must meet strict latency and throughput targets to avoid inference bottlenecks and service degradation. This Skill delivers end-to-end ML operations under 50ms latency with throughput above 2300 requests per second, supporting enterprise-scale deployments.