AI Infrastructure Component

An AI Infrastructure Component is an infrastructure element that provides foundational services and operational capabilities for AI systems through architectural layers and support mechanisms.

• AKA: AI System Component, ML Infrastructure Element, AI Platform Component, AI Architecture Layer, AI Support System.
• Context:
  • It can typically enable Model Deployment through serving infrastructure with runtime environments.
  • It can typically support Data Pipelines via processing frameworks with storage systems.
  • It can typically provide Compute Resources through hardware abstraction with resource scheduling.
  • It can typically facilitate Model Training via training infrastructure with optimization frameworks.
  • It can typically manage System Monitoring through observability platforms with metric collection.
  • ...
  • It can often implement Security Controls via access management with encryption services.
  • It can often enable Scalability Features through elastic scaling with load balancing.
  • It can often support Integration Capabilities via API gateways with connector frameworks.
  • It can often provide Operational Tools through management consoles with automation scripts.
  • ...
  • It can range from being a Low-Level Infrastructure Component to being a High-Level Infrastructure Component, depending on its abstraction level.
  • It can range from being a Core Infrastructure Component to being an Optional Infrastructure Component, depending on its system criticality.
  • It can range from being a Generic Infrastructure Component to being a Specialized Infrastructure Component, depending on its use case specificity.
  • It can range from being a Standalone Infrastructure Component to being an Integrated Infrastructure Component, depending on its dependency model.
  • ...
  • It can integrate with Cloud Platforms for resource provision.
  • It can connect to Container Orchestration Systems for deployment management.
  • It can interface with ML Platforms for model lifecycle management.
  • ...
• Examples:
  • Model Serving Components, such as:
    • Model Server for inference serving.
    • Model Registry for model versioning.
    • Agentic Coding Model Harness for coding model orchestration.
  • Data Infrastructure Components, such as:
    • Feature Store for feature management.
    • Data Lake for data storage.
    • Vector Database for embedding storage.
  • Training Infrastructure Components, such as:
    • Training Cluster for distributed training.
    • Experiment Tracker for training monitoring.
  • ...
• Counter-Examples:
  • Application Software, which provides end-user functionality rather than infrastructure support.
  • Business Logic, which implements domain rules rather than system capabilities.
  • User Interface, which handles user interaction rather than infrastructure services.
• See: AI System Architecture, ML Infrastructure, Cloud Infrastructure, System Component, Platform Architecture, DevOps Infrastructure, Container Platform, Microservice Architecture, Distributed System.

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