/install ml-ops
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.
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.
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install ml-ops - 安装完成后，直接呼叫该 Skill 的名称或使用
/ml-ops触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
Ml Ops 是什么？
Deep MLOps workflow—reproducible training, experiment tracking, packaging, deployment, monitoring (drift, performance), governance, and rollback for ML. Use... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 141 次。
如何安装 Ml Ops？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install ml-ops」即可一键安装，无需额外配置。
Ml Ops 是免费的吗？
是的，Ml Ops 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Ml Ops 支持哪些平台？
Ml Ops 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Ml Ops？
由 clawkk（@clawkk）开发并维护，当前版本 v1.0.0。