Agent skill · data analytics · borghei
ml-ops-engineer
MLOps across model deployment, ML pipelines, monitoring, and feature stores. Use when deploying models to production, building training pipelines, setting up drift detection, configuring feature stores, or automating ML CI/CD workflows.
Why this skill is useful
Provides specific workflows and scripts for deploying ML models that the AI wouldn't reliably generate on its own.
What it needs
Requires airflow, fastapi, kubeflow, kubernetes, mlflow installed locally. About 7k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
MLOps Engineer The agent operates as a senior MLOps engineer, deploying models to production, orchestrating training pipelines, monitoring model health, managing feature stores, and automating ML CI/CD. Clarify First Before deploying, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Serving mode + latency SLA — real-time (FastAPI/K8s) or batch, and the P99 target (drives the entire deployment architecture) [ ] Current MLOps maturity — manual, pipeline, CI/CD, or full (identifies the highest-impact gap to close first) [ ] Model artifact + registry/infra — framework, where it is stored, and target platform (MLflow, K8s) (drives the serving and registry config) [ ] Monitoring thresholds — drift and accuracy-drop limits plus check cadence (drives alert rules and drift detection) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Workflow 1. Assess ML maturity -- Determine the current level (manual notebooks vs. automated pipelines vs. full CI/CD). Identify the highest-impact gap to close first. 2. Build or extend training pipeline -- Define fetch-data, validate, preprocess, train, evaluate stages. Use Kubeflow, Airflow, or equivalent. Gate deployment on an accuracy threshold (e.g., > 0.85). 3. Deploy model for serving -- Choose real-time (FastAPI + K8s) or batch (Spark/Parquet) based on latency requirements. Configure health checks, autoscaling, and resource limits. 4. Register in model registry -- Log parameters, metrics, and artifacts in MLflow. Transition the winning version to Production stage; archive the previous version. 5. Instrument monitoring -- Set up latency (P50/P95/P99), error rate, prediction-distribution, and feature-drift dashboards. Configure alerting thresholds. 6. Validate end-to-end -- Run smoke tests against the serving endpoint. Confirm monitoring dashboards populate. Verify rollback procedure works. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/ml-ops-engineer