Claude Code Plugins

Community-maintained marketplace

Feedback

mlops-patterns

@colingwuyu/optaic-trading
0
0

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.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name mlops-patterns
description 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.

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 signals Filtered signal set

Implementation Workflow

1. Create MLModuleDef (5 Components)

MLModelDef/
├── model/           # Model architecture + hyperparameter schema
├── training/        # Trainer + evaluator
├── inference/       # Predictor + batch inference
├── monitoring/      # Data drift + performance monitoring
├── tests/           # Test suite for all components
└── docs/            # Documentation

See references/mlmodule-structure.md.

2. Create Model Instance

Compose MLModuleDef + datasets + config. See references/model-instance.md.

3. Implement Pipelines

  • TrainingPipeline → reads datasets, produces ModelVersion
  • InferencePipeline → reads features + model, writes predictions
  • MonitoringPipeline → reads data/preds, emits metrics/alerts

See references/mlops-pipelines.md.

4. Integrate with Registry

See references/model-registry.md.

5. Create UI Components (MLOps Center)

Two views required:

  • Model Instance View - registered models with configs
  • Execution View - training, registry, inference, monitoring

See references/mlops-center-ui.md.

Critical Rules

  1. 5-component structure - MLModuleDef must have model, training, inference, monitoring, tests
  2. Activity emission - All runs emit activities (training, inference, monitoring)
  3. Lineage tracking - Link dataset versions → model version → prediction dataset
  4. Guardrails - Validate model outputs (e.g., signal bounds)
  5. PIT correctness - No lookahead in training or inference

Tech Stack

Tool Purpose Mode
MLflow Experiment tracking, model registry Optional (--with-mlflow)
Evidently Data drift, performance monitoring, test suites Always available
WhyLogs Lightweight data profiling Optional
Prefect Workflow orchestration Optional (--with-prefect)

Unified ML SDK (optaic.mlops)

All MLOps infrastructure is wrapped in a unified SDK for seamless development:

from optaic.mlops import tracking, registry, monitoring, pipeline
from optaic.mlops.base import BaseModel, BaseTrainer
from optaic.mlops.data import load_dataset

Key modules:

  • tracking - Experiment logging (wraps MLflow)
  • registry - Model versioning (wraps MLflow Model Registry)
  • monitoring - Drift & performance (wraps Evidently)
  • pipeline - Orchestration (wraps Prefect)
  • data - PIT-aware dataset access
  • base - Base classes for model definitions

See references/unified-sdk.md and Blueprint section 8.9.

Reference Files