Enterprise AI/ML Platform Architecture: From Data to Production

Complete reference architecture for building scalable AI/ML platforms covering feature stores, model training, MLOps, model serving, and monitoring across Databricks, SageMaker, and Vertex AI.

GT
Gonnect TeamJanuary 14, 202520 min readView on GitHub
MLflowFeature StoreSageMakerVertex AIDatabricksKubeflow
Table of Contents

Introduction to Modern ML Platforms

Enterprise AI/ML platforms have evolved from experimental notebook environments into sophisticated, production-grade systems that serve millions of predictions daily. The modern ML platform is not a single tool but an orchestrated ecosystem of components spanning data preparation, feature engineering, model training, deployment, and continuous monitoring.

The key insight driving modern ML platform architecture is that ML systems are fundamentally different from traditional software systems. While traditional applications process inputs deterministically, ML systems learn from data, degrade over time, and require continuous retraining. This demands infrastructure that treats data as a first-class citizen, enables rapid experimentation, and provides robust operational capabilities.

A well-designed enterprise ML platform addresses five core challenges:

  1. Feature Management: Ensuring consistent, reusable features across training and serving
  2. Reproducibility: Guaranteeing that experiments can be recreated exactly
  3. Scalability: Supporting models from prototype to production at any scale
  4. Governance: Maintaining compliance, lineage, and auditability
  5. Operational Excellence: Enabling reliable deployment, monitoring, and incident response

End-to-End ML Platform Architecture

The following architecture illustrates a comprehensive ML platform that integrates all components from data ingestion to production serving:

Enterprise ML Platform Architecture

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Platform Component Overview

LayerComponentsPurpose
Data SourcesTransactional DB, Data Warehouse, Streaming EventsRaw data inputs
IngestionCDC Pipeline, Batch Ingestion, Stream ProcessingData collection and transformation
Feature EngineeringSpark, dbt, Feature StoreFeature computation and storage
Training PlatformMLflow, Kubeflow, HPO, Distributed TrainingModel development and experimentation
Model RegistryVersion Management, Metadata, LineageModel artifact management
Serving LayerReal-time, Batch, Streaming InferenceProduction model deployment
ObservabilityData Quality, Model Performance, Drift DetectionContinuous monitoring
GovernanceLineage, Compliance, Access ControlPolicy and audit

Feature Store Architecture

The Feature Store is the foundational component of any production ML platform. It solves the critical problem of feature consistency between training (offline) and serving (online) environments while enabling feature reuse across teams and models.

Feature Store Architecture

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Key Feature Store Capabilities

CapabilityDescriptionImplementation
Offline StoreHistorical feature data for trainingDelta Lake, Parquet on S3/GCS
Online StoreLow-latency feature servingRedis, DynamoDB, Bigtable
Feature CatalogDiscovery and documentationFeast Registry, Tecton Catalog
Point-in-Time JoinsPrevent data leakageTime-travel queries
Feature VersioningSchema evolution supportFeature version management

Feature Store Implementation

from feast import FeatureStore, Entity, Feature, FeatureView, FileSource
from datetime import timedelta

# Define entity
customer = Entity(
    name="customer_id",
    value_type=ValueType.STRING,
    description="Customer identifier"
)

# Define feature source
customer_stats_source = FileSource(
    path="s3://feature-store/customer_stats.parquet",
    timestamp_field="event_timestamp",
    created_timestamp_column="created_timestamp"
)

# Define feature view
customer_features = FeatureView(
    name="customer_features",
    entities=["customer_id"],
    ttl=timedelta(days=90),
    features=[
        Feature(name="total_purchases", dtype=ValueType.FLOAT),
        Feature(name="avg_order_value", dtype=ValueType.FLOAT),
        Feature(name="days_since_last_order", dtype=ValueType.INT32),
        Feature(name="lifetime_value", dtype=ValueType.FLOAT),
        Feature(name="churn_risk_score", dtype=ValueType.FLOAT),
    ],
    online=True,
    batch_source=customer_stats_source,
    tags={"team": "customer-analytics", "tier": "gold"}
)

# Materialize features to online store
store = FeatureStore(repo_path=".")
store.materialize_incremental(end_date=datetime.now())

# Get online features for inference
features = store.get_online_features(
    features=["customer_features:lifetime_value", "customer_features:churn_risk_score"],
    entity_rows=[{"customer_id": "C12345"}]
).to_dict()

Model Serving Patterns

Production ML systems require different serving patterns based on latency requirements, data freshness needs, and infrastructure constraints.

Model Serving Patterns

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Serving Pattern Comparison

PatternLatencyUse CasesInfrastructure
Real-time< 100msRecommendations, fraud detectionKServe, Seldon, SageMaker Endpoint
BatchMinutes-HoursReport generation, bulk scoringSpark, Airflow scheduled jobs
StreamingSecondsAnomaly detection, real-time analyticsFlink, Kafka Streams
Edge< 10msMobile apps, IoT devicesTensorRT, ONNX Runtime

Real-time Serving with KServe

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: fraud-detection-model
  namespace: ml-serving
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: "s3://models/fraud-detection/v3"
      resources:
        requests:
          cpu: "1"
          memory: "2Gi"
        limits:
          cpu: "2"
          memory: "4Gi"
    minReplicas: 2
    maxReplicas: 10
    scaleTarget: 80
    scaleMetric: concurrency
  transformer:
    containers:
      - name: feature-transformer
        image: myregistry/feature-transformer:v1
        env:
          - name: FEATURE_STORE_URL
            value: "http://feast-server:6566"

MLOps CI/CD Pipeline

Continuous integration and deployment for ML models requires specialized pipelines that handle data validation, model training, evaluation, and staged rollouts.

MLOps CI/CD Pipeline

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CI/CD Pipeline Stages

StageActivitiesTools
Data ValidationSchema checks, drift detectionGreat Expectations, TFX Data Validation
Feature EngineeringFeature computation, validationFeast, Tecton, dbt
Model TrainingTraining, hyperparameter tuningMLflow, Kubeflow, Vertex AI
Model EvaluationPerformance metrics, fairness checksMLflow, Evidently AI
Model RegistryVersioning, approval workflowMLflow Registry, Vertex Model Registry
DeploymentCanary, blue-green, shadowKServe, Seldon, ArgoCD
MonitoringPerformance tracking, alertingPrometheus, Grafana, Evidently

GitHub Actions ML Pipeline

name: ML Pipeline
on:
  push:
    paths:
      - 'models/**'
      - 'features/**'
  workflow_dispatch:
    inputs:
      model_name:
        description: 'Model to train'
        required: true

jobs:
  data-validation:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Validate Data Schema
        run: |
          python -m great_expectations checkpoint run data_quality
      - name: Check Feature Drift
        run: |
          python scripts/check_feature_drift.py

  train-model:
    needs: data-validation
    runs-on: [self-hosted, gpu]
    steps:
      - uses: actions/checkout@v3
      - name: Train Model
        env:
          MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}
        run: |
          python train.py --model-name ${{ github.event.inputs.model_name }}
      - name: Evaluate Model
        run: |
          python evaluate.py --threshold 0.85

  register-model:
    needs: train-model
    runs-on: ubuntu-latest
    steps:
      - name: Register to Model Registry
        run: |
          mlflow models register \
            --model-uri runs:/$RUN_ID/model \
            --name ${{ github.event.inputs.model_name }}

  deploy-staging:
    needs: register-model
    environment: staging
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to Staging
        run: |
          kubectl apply -f k8s/staging/inference-service.yaml
      - name: Run Integration Tests
        run: |
          pytest tests/integration/ -v

  deploy-production:
    needs: deploy-staging
    environment: production
    runs-on: ubuntu-latest
    steps:
      - name: Canary Deployment
        run: |
          kubectl apply -f k8s/production/canary-deployment.yaml
      - name: Monitor Canary
        run: |
          python scripts/monitor_canary.py --duration 30m --threshold 0.01
      - name: Full Rollout
        run: |
          kubectl apply -f k8s/production/full-deployment.yaml

Model Training Infrastructure

Distributed training enables scaling model training across multiple GPUs and nodes for large models and datasets.

Distributed Training with PyTorch

import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler

def setup_distributed():
    dist.init_process_group(backend="nccl")
    local_rank = int(os.environ["LOCAL_RANK"])
    torch.cuda.set_device(local_rank)
    return local_rank

def train_distributed(model, train_dataset, epochs, batch_size):
    local_rank = setup_distributed()
    model = model.to(local_rank)
    model = DDP(model, device_ids=[local_rank])

    sampler = DistributedSampler(train_dataset)
    dataloader = DataLoader(
        train_dataset,
        batch_size=batch_size,
        sampler=sampler,
        num_workers=4,
        pin_memory=True
    )

    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    scaler = torch.cuda.amp.GradScaler()

    for epoch in range(epochs):
        sampler.set_epoch(epoch)
        model.train()

        for batch in dataloader:
            optimizer.zero_grad()

            with torch.cuda.amp.autocast():
                outputs = model(batch["input_ids"].to(local_rank))
                loss = outputs.loss

            scaler.scale(loss).backward()
            scaler.step(optimizer)
            scaler.update()

        if local_rank == 0:
            mlflow.log_metric("train_loss", loss.item(), step=epoch)

    dist.destroy_process_group()

Model Monitoring and Observability

Production ML systems require continuous monitoring for data quality, model performance, and drift detection.

Monitoring Metrics

CategoryMetricsThresholds
Data QualityMissing values, schema violations< 1% missing
Feature DriftPSI, KL divergencePSI < 0.1
Model PerformanceAccuracy, AUC, F1> baseline
Prediction DriftOutput distribution shiftKS test p > 0.05
LatencyP50, P95, P99P99 < SLA
ThroughputRequests/second> capacity

Prometheus Monitoring Rules

groups:
  - name: ml-model-alerts
    rules:
      - alert: ModelAccuracyDegraded
        expr: |
          ml_model_accuracy{model="fraud_detection"} < 0.85
        for: 30m
        labels:
          severity: warning
        annotations:
          summary: "Model accuracy below threshold"

      - alert: FeatureDriftDetected
        expr: |
          ml_feature_psi{feature=~".*"} > 0.1
        for: 1h
        labels:
          severity: warning
        annotations:
          summary: "Feature drift detected: {{ $labels.feature }}"

      - alert: PredictionLatencyHigh
        expr: |
          histogram_quantile(0.99, ml_prediction_latency_bucket) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "P99 prediction latency exceeds 100ms"

Platform Comparison

CapabilityDatabricksSageMakerVertex AI
ComputeSpark clusters, GPUManaged instancesGKE, TPU
Feature StoreFeature StoreFeature StoreVertex Feature Store
TrainingMLflow, AutoMLTraining jobs, HPOVertex Training
RegistryUnity CatalogModel RegistryModel Registry
ServingModel ServingEndpointsVertex Endpoints
PipelinesWorkflowsPipelinesVertex Pipelines
MonitoringLakehouse MonitoringModel MonitorVertex Model Monitoring

Best Practices

1. Feature Engineering

  • Reusability: Design features for reuse across models
  • Consistency: Ensure training/serving parity
  • Documentation: Maintain comprehensive feature catalogs
  • Versioning: Track feature schema changes

2. Model Training

  • Reproducibility: Pin dependencies, log hyperparameters
  • Experimentation: Use experiment tracking religiously
  • Validation: Implement cross-validation and holdout sets
  • Efficiency: Leverage distributed training for scale

3. Deployment

  • Staged Rollouts: Use canary and shadow deployments
  • Rollback: Maintain ability to quickly rollback
  • Testing: Implement comprehensive integration tests
  • Documentation: Document model behavior and limitations

4. Monitoring

  • Proactive: Set up drift detection before issues occur
  • Comprehensive: Monitor data, features, and predictions
  • Alerting: Define clear alerting thresholds and escalation
  • Feedback Loops: Implement mechanisms to capture ground truth

Conclusion

Building an enterprise ML platform requires careful orchestration of multiple components spanning data management, feature engineering, model training, deployment, and monitoring. The key success factors are:

  1. Feature Store as the foundation for consistent features
  2. MLOps pipelines for automated training and deployment
  3. Multiple serving patterns to match business requirements
  4. Comprehensive monitoring for operational excellence
  5. Strong governance for compliance and auditability

The architecture presented provides a reference implementation that can be adapted to your organization's specific needs, whether using cloud-native services like SageMaker or Vertex AI, or building on open-source components like MLflow and Kubeflow.

Further Reading

For complete implementation examples, visit the AIMLPlatform GitHub repository.