Engineering Advanced MLOpsMLDeployment

MLOps Engineer

MLOps: training pipelines, feature stores, model registry, deployment, monitoring, rollback, and governance for production ML systems.

An MLOps skill for teams moving from experimentation to dependable production ML. It emphasizes versioned training inputs, promotion gates, safe rollout patterns, monitoring for drift and quality, and governance that keeps model releases auditable.

Added Apr 12, 2026

$npx skills add johnefemer/skillfish --skill mlops-engineer

Capabilities Get Started Models & Context Works Great With

What This Skill Can Do

Concrete capabilities you get when you install this skill.

Version data, features, code, config, and model artifacts together

Build automated training, validation, and registry promotion pipelines

Design deployment and rollback workflows for production models

Choose between batch, online, shadow, canary, and champion-challenger rollout

Monitor drift, latency, calibration, and business outcomes after release

Define governance, auditability, and release gates for high-impact ML systems

Get Started

How to install and use this skill in your preferred environment.

Skills are designed for AI coding agents (Claude Code, Cursor, Windsurf) and IDE-based workflows where the agent can read files, run scripts, and act on your codebase.

Models & Context

Which AI models and context windows work best with this skill.

Recommended Models

Claude Sonnet or GPT-4o recommended. Larger context helps when pipelines, registry rules, and rollout policy must be reasoned about together.

Context Window

SKILL.md is concise (~3KB). Fits in 32K context; full ML delivery lifecycle planning benefits from 64K+.

Pro tips for best results

1

Be specific

Include numbers — users, budget, RPS — so the skill can size the architecture.

2

Share constraints

Compliance needs, team size, and existing stack all improve the output.

3

Iterate

Start with a high-level design, then ask follow-ups for IaC, cost analysis, or security review.

4

Combine skills

Pair with companion skills below for end-to-end coverage.

Works Great With

These skills complement MLOps Engineer for end-to-end coverage. Install them together for better results.

Senior ML Engineer

ML model deployment, MLOps pipelines, LLM integration, and cost optimization.

MLMLOps

Platform Engineer

Internal developer platform design: golden paths, self-service infrastructure, Backstage/Port catalogs, developer portal, and platform-as-a-product principles.

PlatformDeveloper Experience

Kubernetes Specialist

Kubernetes operations: deployments, services, ingress, Helm charts, operators, RBAC, resource limits, debugging pods, and cluster management for production.

KubernetesHelm

Observability Designer

SLO design, alert optimization, and dashboard generation.

ObservabilitySLO

$ skillfish add johnefemer/skillfish --all # install all skills at once

Ready to try MLOps Engineer?

Install the skill and start getting expert-level guidance in your workflow — any agent, any IDE.