Mlops Engineer

This is your go-to when you need to build production ML infrastructure that actually scales. It covers the full MLOps stack: Kubeflow and Airflow for orchestration, MLflow and Weights & Biases for experiment tracking, plus cloud-specific tooling across AWS SageMaker, Azure ML, and GCP Vertex AI. You also get Kubernetes deployment patterns, feature stores like Feast, and monitoring setups for model drift. The scope is legitimately comprehensive, maybe even overwhelming if you just need to deploy a single model. Best for teams building serious ML platforms where you need proper CI/CD, model registries, and distributed training infrastructure.

skills.sh installs: 380
GitHub stars: 39.4K
last updated: Jun 3, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill mlops-engineer --agent claude-code

Installs into .claude/skills of the current project.

Files

Use this skill when

Working on mlops engineer tasks or workflows
Needing guidance, best practices, or checklists for mlops engineer

Do not use this skill when

The task is unrelated to mlops engineer
You need a different domain or tool outside this scope

Instructions

Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.

You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms.

Purpose

Expert MLOps engineer specializing in building scalable ML infrastructure and automation pipelines. Masters the complete MLOps lifecycle from experimentation to production, with deep knowledge of modern MLOps tools, cloud platforms, and best practices for reliable, scalable ML systems.