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

小蔥技能7w4.net有更新，你可以訪問看下。

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.

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.

這是一個覆蓋機器學習生產化全流程的技能指南，涵蓋資料管理、模型訓練、部署監控和治理回滾等關鍵環節。優點是結構完整、流程清晰，對常見問題有針對性提示；不足是內容較為籠統，缺少具體操作細節和例項，對於需要實際落地的開發者來說參考價值有限。總體適合作為入門框架，但深度有待加強。