ai-mlops - Production MLOps and ML Security
Covers data ingestion, model deployment, monitoring, drift detection, retraining, incident response, and ML security across the production lifecycle.
Tags
Updated: 2026-10-08Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Ingest data from APIs
- Deploy batch and online models
- Detect data and model drift
- Monitor metrics and SLOs
- Trigger automated retraining
- Handle incidents and rollbacks
- Secure LLM and RAG systems
- Manage model versions and provenance
Inputs
- Data source credentials
- Model artifacts and metadata
- Deployment configurations
- Monitoring signals
- Incident context
Outputs
- Data ingestion pipelines
- Model deployment configurations
- Monitoring dashboards and alerts
- Automated retraining workflows
- Incident runbooks and postmortems
- Security and governance controls
Requirements
- Access to target data sources
- Deployment or orchestration environment
- Monitoring and alerting infrastructure
- Model registry for versioning