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

Objective: Establish comprehensive ML systems architecture covering the full lifecycle from data ingestion to model deployment and monitoring. When you need feature stores, when you want experiment governance, when you need reproducibility—this guide provides the complete framework.

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.