Ml Ops Engineer

You're getting a full MLOps stack here: model deployment workflows, drift detection with KS tests, FastAPI serving endpoints with health checks, Kubernetes manifests with HPA rules, Feast feature store integration, and MLflow experiment tracking. The maturity model is helpful for figuring out where you are (manual notebooks vs. full CI/CD) and what to tackle next. The code is production-grade, complete with resource limits, rollback procedures, and alert thresholds. If you're moving past the "train in a notebook, pray in production" phase, this gives you the scaffolding for proper ML infrastructure. The monitoring section alone (latency percentiles, prediction drift, error rates) will save you from some painful incidents.

skills.sh installs: 175
GitHub stars: 1.1K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add borghei/claude-skills --skill ml-ops-engineer --agent claude-code

Installs into .claude/skills of the current project.

Files

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, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

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).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.