name: ml-ops description: Deep MLOps workflow—reproducible training, experiment tracking, packaging, deployment, monitoring (drift, performance), governance, and rollback for ML. Use when shipping models to production or hardening ML pipelines.

MLOps (Deep Workflow)

MLOps connects research velocity to production reliability: version data, code, and artifacts together; monitor behavior after deploy.

When to Offer This Workflow

Trigger conditions:

• First production model; batch or online serving
• Drift, bias, or latency SLO misses
• Compliance needs for lineage and explainability

Initial offer:

Use six stages: (1) problem & risk class, (2) data & reproducibility, (3) training & evaluation, (4) packaging & deployment, (5) monitoring & feedback, (6) governance & rollback). Confirm batch vs real-time and regulatory tier.

Stage 1: Problem & Risk Class

Goal: Align ML to decision risk (credit, health vs recommendation).

Exit condition: Offline and online success metrics defined.

Stage 2: Data & Reproducibility

Goal: Snapshot training data; deterministic pipelines; PII handling.

Practices

• Feature stores optional but valuable for consistency
• Secrets not in notebooks; orchestrated jobs

Exit condition: Run id reproduces artifact hash within agreed bounds.

Stage 3: Training & Evaluation

Goal: Train/val/test without leakage; time-series splits careful.

小葱技能站7w4.net每天更新，海量AI技能等你发现。

Practices

• Model card with limits and metrics
• Fairness slices where policy requires

Stage 4: Packaging & Deployment

Goal: Immutable artifacts; canary or shadow before full cutover.

Practices

• Model + preprocessing code version pinned together

Exit condition: Rollback to previous artifact id documented.

Stage 5: Monitoring & Feedback

Goal: Data drift, concept drift, latency; business KPIs tied to model decisions.

Practices

• Human review queue for low-confidence predictions when needed

Stage 6: Governance & Rollback

Goal: Approvals for retrain/deploy; audit trail; A/B for big changes.

Final Review Checklist

• [ ] Offline metrics aligned with business risk
• [ ] Data and code reproducibility
• [ ] Packaged artifacts with versioning and rollback
• [ ] Online monitoring and drift strategy
• [ ] Governance and approval path

Tips for Effective Guidance

• Training-serving skew is a top bug—feature parity tests help.
• Offline accuracy ≠ online business outcome.
• Fairness needs explicit slices—not one headline number.

Handling Deviations

• LLM-heavy products: lean on eval harnesses and prompt versioning (see llm-evaluation).
• Tiny teams: start with artifact registry + dashboards before a full feature store.

这是一个覆盖机器学习生产化全流程的技能指南，涵盖数据管理、模型训练、部署监控和治理回滚等关键环节。优点是结构完整、流程清晰，对常见问题有针对性提示；不足是内容较为笼统，缺少具体操作细节和实例，对于需要实际落地的开发者来说参考价值有限。总体适合作为入门框架，但深度有待加强。