mlops-patterns
Follow these patterns when implementing MLOps features in OptAIC. Use for ML model definitions (5-component structure), model instances, training/inference pipelines, model registry, and monitoring. Covers signal models, macro regime models, relevance models, and signal combining/filtering models.
npx skills add majiayu000/claude-skill-registry --skill mlops-patterns --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MLOps Implementation Patterns Guide for implementing MLOps features that integrate with OptAIC's resource-based architecture. ## When to Use Apply when: - Creating ML Model Definitions (MLModuleDef) with 5 code components - Implementing Model Instances in MLOps Center - Building training, inference, or monitoring pipelines - Integrating with model registry (MLflow or internal) - Implementing model categories (signal, regime, relevance, combining) ## MLOps Three-Tier Model ``` MLModuleDef (Definition) ModelInstance (Config) Execution (Runs) ──────────────────────── ────────────────────── ───────────────── XGBSignalModelDef → SPX_Alpha_Model → TrainingRun (5 code components) (datasets + config) InferenceRun MonitoringRun ↓ ModelVersion ``` ## ML Model Categories | Category | Purpose | Typical Outputs | |----------|---------|-----------------| | **Signal Model** | Generate alpha signals | Signal dataset [-1, 1] | | **Macro Regime Model** | Classify market regimes | Regime labels/probabilities | | **Relevance Model** | Score feature importance | Relevance scores | | **Signal Combining Model** | Combine multiple signals | Combined signal | | **Signal Filtering Model** | Filter/rank si
- When to Use
- MLOps Three-Tier Model
- ML Model Categories
- Implementation Workflow
- 1. Create MLModuleDef (5 Components)
- 2. Create Model Instance
- 3. Implement Pipelines
- 4. Integrate with Registry
- 5. Create UI Components (MLOps Center)
- Critical Rules
- Tech Stack
- Unified ML SDK (optaic.mlops)
- Reference Files
What does the mlops-patterns skill do?
Follow these patterns when implementing MLOps features in OptAIC. Use for ML model definitions (5-component structure), model instances, training/inference pipelines, model registry, and monitoring. Covers signal models, macro regime models, relevance models, and signal combining/filtering models.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlops-patterns --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.