Intermediate

Core Architecture Components

Explore the fundamental building blocks of an enterprise AI reference architecture, from compute and storage infrastructure to security controls and integration interfaces.

Component Categories

An AI reference architecture organizes its building blocks into distinct categories that work together to support the full AI lifecycle:

CategoryPurposeKey Technologies
ComputeProcessing power for training and inferenceGPUs, TPUs, CPU clusters, serverless functions
StoragePersistent data and model artifact storageObject storage, data lakes, model registries
NetworkingConnectivity between componentsService mesh, API gateways, load balancers
SecurityAccess control and data protectionIAM, encryption, secret management, audit logs
OrchestrationWorkflow management and schedulingKubernetes, Airflow, Kubeflow, Step Functions

Compute Infrastructure

AI workloads have unique compute requirements that differ significantly between training and inference phases:

  1. Training Compute

    GPU or TPU clusters optimized for high-throughput matrix operations. Supports distributed training across multiple nodes with high-bandwidth interconnects for gradient synchronization.

  2. Inference Compute

    Optimized for low-latency predictions with cost-efficient hardware. Ranges from GPU instances for large models to CPU-based or serverless options for lighter workloads.

  3. Development Compute

    Interactive environments for data scientists including Jupyter notebooks, IDE integrations, and shared development clusters with resource quotas.

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Cost Optimization: Use spot or preemptible instances for training workloads with checkpointing enabled. Reserve capacity only for production inference endpoints that require guaranteed availability.

Storage Architecture

Data Lake

Centralized repository for raw and processed data in various formats. Supports structured, semi-structured, and unstructured data at scale.

Feature Store

Managed repository for curated ML features with versioning, lineage tracking, and both online and offline serving capabilities.

Model Registry

Versioned storage for trained model artifacts with metadata, performance metrics, lineage information, and deployment status tracking.

Artifact Store

Storage for experiment artifacts including training logs, evaluation reports, configuration files, and intermediate pipeline outputs.

Security Components

Security is not a separate layer but is woven throughout every component of the architecture:

  • Identity and Access Management: Role-based access control for data, models, and infrastructure with least-privilege principles
  • Data Encryption: Encryption at rest and in transit for all data stores, model artifacts, and inter-service communication
  • Network Security: VPC isolation, private endpoints, firewall rules, and network policies for cluster-level segmentation
  • Audit Logging: Comprehensive audit trails for data access, model changes, deployment actions, and configuration modifications
  • Secret Management: Centralized secret storage for API keys, credentials, and certificates with automatic rotation
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Looking Ahead: In the next lesson, we will focus on the data layer in detail, covering data ingestion pipelines, feature engineering, data quality, and governance patterns.