ml-engineerlisted

Build production ML systems: model training pipelines, serving infrastructure, performance optimization, and automated retraining. Use when: (1) designing or building ML pipelines (data validation → training → deployment), (2) optimizing model training (hyperparameter search, distributed training, checkpointing), (3) deploying models to production (REST/gRPC endpoints, batch/stream processing, canary releases), (4) setting up ML monitoring (prediction drift, feature drift, performance decay), (5) implementing feature engineering or feature stores, (6) automating retraining triggers, (7) debugging model performance or serving latency issues. Triggers on: ML pipeline, model training, model serving, feature engineering, hyperparameter tuning, model deployment, inference optimization, model monitoring, MLOps, retraining.
OpenCoven/coven · ★ 47 · AI & Automation · score 76

Install: claude install-skill OpenCoven/coven

# ML Engineer Build and operate production ML systems across the full lifecycle: data → features → training → validation → deployment → monitoring → retraining. ## Core Workflow ### 1. System Analysis Before building anything: 1. **Define the problem** — classification, regression, ranking, generation, etc. 2. **Assess data** — volume, quality, drift patterns, labeling status 3. **Set targets** — accuracy, latency (<50ms inference), training time (<4h), cost ceiling 4. **Map infrastructure** — compute (GPU/CPU), storage, orchestration, serving platform 5. **Choose deployment strategy** — real-time, batch, streaming, edge 6. **Plan monitoring** — what metrics, what thresholds, who gets paged ### 2. Pipeline Development Build modular, versioned pipelines. Each stage should be independently testable and retriable. ``` Data Validation → Feature Engineering → Training → Validation → Deployment → Monitoring ↑ | └──────────────────── Retraining Trigger ←──────────────────────────────────┘ ``` **Pipeline principles:** - Data validation FIRST — catch schema drift, missing values, distribution shifts before training - Version everything: data, features, models, configs, code - Each stage writes artifacts to a versioned store (MLflow, DVC, W&B) - Fail fast with clear error messages; never silently produce bad models See `references/pipelines.md` for stage-by-stage implementation patterns. ###