AI-System Software Architecture Model

An AI-System Software Architecture Model is a software architecture model that organizes AI system components into specialized software architecture layers (to enable artificial intelligence capabilitys through layered organization).

• AKA: AI Software Architecture, AI Architecture Pattern.
• Context:
  • It can typically be instantiated in AI-System Software Architecture Artifacts.
  • It can typically organize AI Components through layer hierarchys.
  • It can typically manage Model Lifecycle through development layers.
  • It can typically handle AI Data Flow through pipeline layers.
  • It can typically support Model Serving through inference layers.
  • It can typically coordinate AI Operations through MLOps layers.
  • ...
  • It can often implement Model Training through training layers.
  • It can often provide Data Processing through transformation layers.
  • It can often manage Model Deployment through serving layers.
  • It can often support Model Monitoring through observability layers.
  • ...
  • It can range from being a Research AI Architecture to being a Production AI Architecture, depending on its deployment context.
  • It can range from being a Monolithic AI Architecture to being a Distributed AI Architecture, depending on its system distribution.
  • It can range from being a Single Model Architecture to being a Multi Model Architecture, depending on its model complexity.
  • ...
  • It can integrate with Enterprise Software Systems through business integrations.
  • It can connect to Cloud Platforms through infrastructure services.
  • It can utilize Data Platforms through storage services.
  • ...
• Examples:
  • AI Core Architectures, such as:
    • Model Development Architectures for model creation systems, such as:
      • Training Pipeline Architectures for model training workflows.
      • Model Configuration Architectures for model setup workflows.
      • Model Evaluation Architectures for model validation workflows.
    • AI Agent Architectures for agent systems, such as:
      • Agent Framework Architectures for agent development workflows.
      • Agent Interaction Architectures for agent communication workflows.
      • Agent Memory Architectures for agent state workflows.
    • AI Service Architectures for inference systems, such as:
      • Real-Time Inference Architectures for online prediction workflows.
      • Batch Processing Architectures for offline prediction workflows.
      • Edge Deployment Architectures for edge inference workflows.
  • AI Supporting Architectures, such as:
    • AI Infrastructure Architectures for compute workflows.
    • AI Security Architectures for protection workflows.
    • AI Integration Architectures for connectivity workflows.
  • ...
• Counter-Examples:
  • Traditional Software Architecture, which lacks AI-specific capabilitys.
  • Data Platform Architecture, which focuses on data management rather than AI processing.
  • Analytics Platform Architecture, which handles business intelligence rather than machine learning.
• See: AI Architecture Layer, ML System Design, AI Platform Design, Model Development Lifecycle.

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