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ML Systems Architecture: Feature Stores, Model Serving, Experiment Governance, and Cross-System Reproducibility

Introduction

ML systems require careful architecture to ensure reproducibility, governance, and operational excellence. This guide establishes patterns for feature stores, model serving, experiment governance, and cross-system reproducibility across all ML workloads.

What This Guide Covers: - Full lifecycle architecture: ingest → transform → feature store → model training → registry → deployment → monitoring - MLflow + ONNX + browser inference + geospatial ML pipelines - Reproducibility standards across CPUs/GPUs/edge devices - Metadata governance for experiment lineage - Feature store schema contracts - Model drift detection and automated rollback - Multi-environment ML governance (dev/stage/prod/air-gapped prod)

Prerequisites: - Understanding of machine learning workflows and MLOps - Familiarity with MLflow, ONNX, and model serving - Experience with feature stores and experiment tracking

Related Documents: This document integrates with: - Embeddings & Vector Databases - Vector storage for ML - MCP + FastAPI Full Stack - ML serving infrastructure - ONNX Model Optimization - Model optimization - Data Lineage Contracts - ML experiment lineage - Chaos Engineering Governance - ML system resilience - Multi-Region DR Strategy - ML system DR - Semantic Layer Engineering - ML semantic layers

The Philosophy of ML Systems Architecture

ML Lifecycle Principles

Principle 1: Reproducibility - Version all artifacts - Track all dependencies - Enable exact reproduction

Principle 2: Governance - Track all experiments - Enforce policies - Audit all changes

Principle 3: Observability - Monitor model performance - Track data drift - Alert on anomalies

Full Lifecycle Architecture

Lifecycle Pipeline

Architecture: 

graph LR
    Ingest["Data Ingestion"]
    Transform["Transformation"]
    FeatureStore["Feature Store"]
    Training["Model Training"]
    Registry["Model Registry"]
    Deployment["Deployment"]
    Monitoring["Monitoring"]

    Ingest --> Transform
    Transform --> FeatureStore
    FeatureStore --> Training
    Training --> Registry
    Registry --> Deployment
    Deployment --> Monitoring
    Monitoring --> FeatureStore

Implementation

Pipeline Definition: 

# ML lifecycle pipeline
class MLLifecyclePipeline:
    def __init__(self):
        self.ingest = DataIngestion()
        self.transform = DataTransformation()
        self.feature_store = FeatureStore()
        self.training = ModelTraining()
        self.registry = ModelRegistry()
        self.deployment = ModelDeployment()
        self.monitoring = ModelMonitoring()

    def run(self, experiment: Experiment) -> Model:
        """Run full ML lifecycle"""
        # Ingest
        data = self.ingest.ingest(experiment.data_source)

        # Transform
        transformed = self.transform.transform(data)

        # Store features
        features = self.feature_store.store(transformed)

        # Train
        model = self.training.train(features, experiment.config)

        # Register
        registered = self.registry.register(model, experiment)

        # Deploy
        deployed = self.deployment.deploy(registered)

        # Monitor
        self.monitoring.monitor(deployed)

        return deployed

MLflow Integration

MLflow Setup

Configuration: 

# MLflow configuration
import mlflow

mlflow.set_tracking_uri("http://mlflow-server:5000")
mlflow.set_experiment("geospatial-ml")

# Log experiment
with mlflow.start_run():
    mlflow.log_params(params)
    mlflow.log_metrics(metrics)
    mlflow.log_model(model, "model")

Experiment Tracking

Pattern: Track all experiments.

Example: 

# Experiment tracking
class ExperimentTracker:
    def track_experiment(self, experiment: Experiment):
        """Track ML experiment"""
        with mlflow.start_run(run_name=experiment.name):
            # Log parameters
            mlflow.log_params(experiment.params)

            # Log metrics
            mlflow.log_metrics(experiment.metrics)

            # Log artifacts
            mlflow.log_artifacts(experiment.artifacts)

            # Log model
            mlflow.log_model(experiment.model, "model")

ONNX Integration

ONNX Model Export

Pattern: Export models to ONNX.

Example: 

# ONNX export
import onnx
from onnx import helper

def export_to_onnx(model, input_shape, output_path):
    """Export model to ONNX"""
    onnx_model = helper.make_model(
        graph=model.graph,
        producer_name="ml-pipeline"
    )

    onnx.save(onnx_model, output_path)

See: ONNX Model Optimization

Browser Inference

Browser Inference Setup

Pattern: Deploy models for browser inference.

Example: 

// Browser inference
import * as ort from 'onnxruntime-web';

async function loadModel(modelPath) {
    const session = await ort.InferenceSession.create(modelPath);
    return session;
}

async function predict(session, input) {
    const results = await session.run(input);
    return results;
}

Geospatial ML Pipelines

Geospatial Pipeline

Pattern: ML pipelines for geospatial data.

Example: 

# Geospatial ML pipeline
class GeospatialMLPipeline:
    def __init__(self):
        self.feature_extractor = GeospatialFeatureExtractor()
        self.model = GeospatialModel()

    def process(self, geospatial_data: GeospatialData) -> Prediction:
        """Process geospatial data"""
        features = self.feature_extractor.extract(geospatial_data)
        prediction = self.model.predict(features)
        return prediction

Reproducibility Standards

Reproducibility Framework

Pattern: Ensure reproducibility across environments.

Example: 

# Reproducibility framework
class ReproducibilityFramework:
    def __init__(self):
        self.version_control = VersionControl()
        self.dependency_tracker = DependencyTracker()
        self.environment_manager = EnvironmentManager()

    def ensure_reproducibility(self, experiment: Experiment) -> ReproducibilityReport:
        """Ensure experiment reproducibility"""
        # Version all artifacts
        versions = self.version_control.version_all(experiment)

        # Track dependencies
        dependencies = self.dependency_tracker.track(experiment)

        # Manage environment
        environment = self.environment_manager.create(experiment)

        return ReproducibilityReport(
            versions=versions,
            dependencies=dependencies,
            environment=environment
        )

Cross-Device Reproducibility

Pattern: Reproduce across CPUs/GPUs/edge devices.

Example: 

# Cross-device reproducibility
class CrossDeviceReproducibility:
    def reproduce(self, model: Model, device: Device) -> ReproducedModel:
        """Reproduce model on device"""
        # Adapt model for device
        adapted = self.adapt_model(model, device)

        # Verify reproducibility
        verified = self.verify_reproduction(adapted, model)

        return ReproducedModel(adapted, verified)

Metadata Governance

Experiment Lineage

Pattern: Track experiment lineage.

Example: 

# Experiment lineage
class ExperimentLineage:
    def track_lineage(self, experiment: Experiment) -> Lineage:
        """Track experiment lineage"""
        lineage = {
            'experiment_id': experiment.id,
            'data_source': experiment.data_source,
            'features': experiment.features,
            'model': experiment.model,
            'dependencies': experiment.dependencies,
            'timestamp': experiment.timestamp
        }

        return Lineage(lineage)

See: Data Lineage Contracts

Feature Store Schema Contracts

Feature Store Contracts

Pattern: Define feature store contracts.

Example: 

# Feature store contract
class FeatureStoreContract:
    def __init__(self):
        self.schema = FeatureSchema()
        self.validation = FeatureValidation()

    def validate_feature(self, feature: Feature) -> bool:
        """Validate feature against contract"""
        return self.validation.validate(feature, self.schema)

Model Drift Detection

Drift Detection

Pattern: Detect model drift.

Example: 

# Model drift detection
class ModelDriftDetector:
    def detect_drift(self, model: Model, data: Data) -> DriftReport:
        """Detect model drift"""
        # Compare predictions
        predictions = model.predict(data)
        baseline_predictions = self.baseline_model.predict(data)

        # Calculate drift
        drift = self.calculate_drift(predictions, baseline_predictions)

        # Generate report
        report = DriftReport(
            drift_score=drift,
            threshold=self.drift_threshold,
            action="rollback" if drift > self.drift_threshold else "monitor"
        )

        return report

Automated Rollback

Pattern: Automatically rollback on drift.

Example: 

# Automated rollback
class AutomatedRollback:
    def rollback(self, model: Model, drift_report: DriftReport):
        """Automatically rollback model"""
        if drift_report.action == "rollback":
            # Get previous model
            previous = self.registry.get_previous_version(model)

            # Deploy previous
            self.deployment.deploy(previous)

            # Alert
            self.alerting.alert("Model rolled back due to drift")

Multi-Environment ML Governance

Environment Configuration

Pattern: Govern ML across environments.

Example: 

# Multi-environment ML governance
ml_governance:
  environments:
    dev:
      model_registry: "mlflow-dev"
      feature_store: "feature-store-dev"
      deployment: "manual"
    staging:
      model_registry: "mlflow-staging"
      feature_store: "feature-store-staging"
      deployment: "automated"
    prod:
      model_registry: "mlflow-prod"
      feature_store: "feature-store-prod"
      deployment: "automated-with-approval"
    air_gapped_prod:
      model_registry: "mlflow-air-gapped"
      feature_store: "feature-store-air-gapped"
      deployment: "manual-with-verification"

Cross-Document Architecture

graph TB
    subgraph ML["ML Systems Architecture<br/>(This Document)"]
        Lifecycle["Lifecycle"]
        FeatureStore["Feature Store"]
        Serving["Model Serving"]
    end

    subgraph Embeddings["Embeddings"]
        Vectors["Vector DB"]
    end

    subgraph MCP["MCP + FastAPI"]
        ServingInfra["Serving"]
    end

    subgraph ONNX["ONNX"]
        Optimization["Optimization"]
    end

    subgraph Lineage["Data Lineage"]
        ExperimentLineage["Experiment Lineage"]
    end

    FeatureStore --> Vectors
    Serving --> ServingInfra
    Lifecycle --> Optimization
    Lifecycle --> ExperimentLineage

    style ML fill:#f3e5f5
    style Embeddings fill:#e1f5ff
    style MCP fill:#fff4e1
    style ONNX fill:#e8f5e9
    style Lineage fill:#ffebee

Checklists

ML Systems Compliance Checklist

  • Lifecycle pipeline defined
  • MLflow integration configured
  • ONNX export enabled
  • Browser inference supported
  • Reproducibility framework active
  • Experiment lineage tracked
  • Feature store contracts defined
  • Drift detection enabled
  • Automated rollback configured
  • Multi-environment governance active

Anti-Patterns

ML Systems Anti-Patterns

No Versioning: 

# Bad: No versioning
model = train_model(data)

# Good: Versioned
model = train_model(data)
versioned = registry.register(model, version="v1.2.3")

No Lineage Tracking: 

# Bad: No lineage
experiment = run_experiment()

# Good: Lineage tracked
experiment = run_experiment()
lineage = track_lineage(experiment)

See Also

This guide establishes comprehensive ML systems architecture patterns. Start with lifecycle design, extend to governance, and continuously monitor and improve model performance.