ml-engineer

# 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. ###...