AI Reference Architecture Intermediate

A well-designed reference architecture provides a proven blueprint for building AI systems. This lesson presents end-to-end architectures that have been validated in production environments, covering the data layer, training infrastructure, model management, and serving systems.

End-to-End ML Platform Architecture

Layer 1: Data Foundation
  Object Storage (S3/GCS/Blob) → Data Lake → Feature Store
  Streaming: Kafka/Kinesis → Stream Processing → Feature Store

Layer 2: Experimentation
  Notebooks (JupyterHub) → Experiment Tracker (MLflow/W&B)
  Shared GPU Pool → Job Scheduler (Kubernetes/Slurm)

Layer 3: Training Pipeline
  Pipeline Orchestrator (Airflow/Kubeflow) → Training Cluster
  Hyperparameter Tuning → Model Evaluation → Model Registry

Layer 4: Serving Infrastructure
  Model Registry → Deployment Pipeline → Inference Cluster
  Load Balancer → API Gateway → Monitoring

Layer 5: Observability
  Metrics Collection → Dashboards → Alerts
  Data Drift Detection → Model Performance Tracking

Provider-Specific Architectures

AWS ML Architecture

• Data - S3 Data Lake + Glue ETL + SageMaker Feature Store
• Training - SageMaker Training Jobs on P5/P4d instances with EFA
• Registry - SageMaker Model Registry with approval workflows
• Serving - SageMaker Endpoints with auto-scaling or EKS with Triton
• Monitoring - SageMaker Model Monitor + CloudWatch

GCP ML Architecture

• Data - BigQuery + Dataflow + Vertex AI Feature Store
• Training - Vertex AI Training on A3/TPU pods
• Registry - Vertex AI Model Registry
• Serving - Vertex AI Endpoints or GKE with Triton
• Monitoring - Vertex AI Model Monitoring + Cloud Monitoring

Key Architectural Decisions