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

code

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)

code

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

•5-component structure - MLModuleDef must have model, training, inference, monitoring, tests
•Activity emission - All runs emit activities (training, inference, monitoring)
•Lineage tracking - Link dataset versions → model version → prediction dataset
•Guardrails - Validate model outputs (e.g., signal bounds)
•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:

python

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

•Unified SDK - optaic.mlops SDK patterns
•MLModuleDef Structure - 5-component package
•Model Instance - Configuration patterns
•MLOps Pipelines - Training/inference/monitoring
•Model Registry - Version management
•MLOps Center UI - Two-view architecture
•MLflow + Evidently Integration - Experiment tracking & monitoring