4. High-Level Architecture

A production AI system can be understood as four interconnected layers, with a fifth cross-cutting layer for observability.

Data Layer

Responsible for ingesting, storing, processing, and serving data for both training and inference.

• Data ingestion: Batch (Airflow) and streaming (Kafka) pipelines.
• Feature store: Centralized repository of features (Feast, Tecton, SageMaker Feature Store).
• Data lake / warehouse: Raw and processed data storage (S3, Snowflake, BigQuery).
• Data versioning: DVC, LakeFS — track dataset versions for reproducibility.

Model Layer

Manages model training, evaluation, packaging, and lifecycle.

• Training infrastructure: Distributed training (PyTorch DDP, DeepSpeed), hyperparameter tuning (Optuna, Ray Tune).
• Model registry: Versioned model storage with metadata (MLflow, W&B, Hugging Face Hub).
• Evaluation: Offline eval on held-out datasets, online eval via A/B testing.
• Compression: Quantization (FP16, INT8), pruning, distillation for deployment.

Serving Layer

Exposes the model to consumers via APIs with low latency and high throughput.

• Inference server: NVIDIA Triton, TorchServe, BentoML, vLLM, TGI.
• API gateway: Authentication, rate limiting, request validation, routing.
• Cache: Feature cache (Redis), prediction cache (for identical requests), model cache (GPU memory).
• Load balancer: Distribute requests across inference replicas.

Orchestration Layer

Coordinates multi-step workflows, agent collaboration, and pipeline execution. Increasingly important in 2026 for agentic AI systems.

• Agent orchestrator: Decomposes tasks, delegates to specialized agents, synthesizes results.
• MCP (Model Context Protocol): Standardized tool and data source integration for LLMs.
• Workflow engine: Airflow, Prefect, Temporal for batch pipelines; LangGraph, CrewAI for agent orchestration.
• Feedback loop: Collects user feedback and model outcomes for retraining and drift detection.

Monitoring & Observability Layer (Cross-Cutting)

Spans all layers to provide visibility into system health, model performance, and business impact.

• Metrics: Prometheus + Grafana dashboards.
• Logging: Structured logs per request for debugging and audit.
• Tracing: Distributed tracing (OpenTelemetry, Jaeger) across microservices and inference calls.
• Drift detection: Evidently AI, WhyLabs, NannyML — continuous monitoring of data and concept drift.

# Layered request flow (recommendation system)
User request → API Gateway → Auth → Feature Fetch (from Feature Store)
→ Cache Check (Redis) → Model Inference → Post-process → Log → Response