ml-engineer
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.
npx skills add majiayu000/claude-skill-registry --skill ml-engineer-skill-404kidwiz-claude-supercode-ski --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Machine Learning Engineer ## Purpose Provides MLOps and production ML engineering expertise specializing in end-to-end ML pipelines, model deployment, and infrastructure automation. Bridges data science and production engineering with robust, scalable machine learning systems. ## When to Use - Building end-to-end ML pipelines (Data → Train → Validate → Deploy) - Deploying models to production (Real-time API, Batch, or Edge) - Implementing MLOps practices (CI/CD for ML, Experiment Tracking) - Optimizing model performance (Latency, Throughput, Resource usage) - Setting up feature stores and model registries - Implementing model monitoring (Drift detection, Performance tracking) - Scaling training workloads (Distributed training) --- --- ## 2. Decision Framework ### Model Serving Strategy ``` Need to serve predictions? │ ├─ Real-time (Low Latency)? │ │ │ ├─ High Throughput? → **Kubernetes (KServe/Seldon)** │ ├─ Low/Medium Traffic? → **Serverless (Lambda/Cloud Run)** │ └─ Ultra-low latency (<10ms)? → **C++/Rust Inference Server (Triton)** │ ├─ Batch Processing? │ │ │ ├─ Large Scale? → **Spark / Ray** │ └─ Scheduled Jobs? → **Airflow / Prefect** │ └─ Edge / Client-side? │ ├─ Mobile? →
- Purpose
- When to Use
- 2. Decision Framework
- Model Serving Strategy
- Training Infrastructure
- Feature Store Decision
- 3. Core Workflows
- Workflow 1: End-to-End Training Pipeline
- Workflow 3: Drift Detection (Monitoring)
- Workflow 5: RAG Pipeline with Vector Database
- 5. Anti-Patterns & Gotchas
- ❌ Anti-Pattern 1: Training-Serving Skew
- ❌ Anti-Pattern 2: Manual Deployments
- ❌ Anti-Pattern 3: Silent Failures
python train.py
What does the ml-engineer skill do?
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-engineer-skill-404kidwiz-claude-supercode-ski --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.