AI-based System Architecture

An AI-based System Architecture is an software-system architecture model for an AI-based system (organizes AI system components).

• Context:
  • It can typically define Component Structure through module organization, interface design, and dependency management.
  • It can typically establish Processing Flow through data pipelines, computation sequences, and feedback loops.
  • It can typically facilitate System Integration through component interoperability, standardized interfaces, and communication protocols.
  • It can typically support Scalability Patterns through resource allocation, distributed processing, and performance optimization.
  • It can typically enable AI Functionality through model deployment, algorithm implementation, and capability orchestration.
  • ...
  • It can often incorporate Development Lifecycle through implementation phases, testing procedures, and deployment stages.
  • It can often address System Governance through security controls, privacy mechanisms, and ethical guidelines.
  • It can often manage Operational Concerns through monitoring capabilitys, maintenance procedures, and reliability enhancements.
  • It can often support Evolution Paths through versioning strategy, upgrade mechanisms, and backward compatibility.
  • ...
  • It can range from being a Simple AI Architecture Model to being a Complex AI Architecture Model, depending on its architectural complexity.
  • It can range from being a Domain-Specific AI Architecture Model to being a General-Purpose AI Architecture Model, depending on its application scope.
  • It can range from being a Monolithic AI Architecture Model to being a Distributed AI Architecture Model, depending on its deployment topology.
  • ...
  • It can integrate with Infrastructure Architecture for computing environments.
  • It can connect to Data Architecture for information management.
  • It can align with Enterprise Architecture for organizational integration.
  • ...
• Examples:
  • AI Architecture Patterns, such as:
    • AI Service Architecture Patterns, such as:
      • Microservice AI Architecture for independent AI service deployment.
      • Serverless AI Architecture for event-driven AI processing.
      • API-centric AI Architecture for AI capability exposure.
    • Data Processing Architecture Patterns, such as:
      • Pipeline AI Architecture for sequential data processing.
      • Lambda Architecture for batch and stream processing.
      • Kappa Architecture for stream-focused processing.
    • Model Deployment Architecture Patterns, such as:
      • Hub-and-Spoke Model Architecture for centralized model management.
      • Edge-Computing Model Architecture for distributed inference.
      • Hybrid Deployment Architecture for balanced processing distribution.
  • AI System Type Architectures, such as:
    • Machine Learning System Architectures, such as:
      • Training Platform Architecture for model development.
      • Inference System Architecture for model application.
      • AutoML Architecture for automated model creation.
    • Natural Language Processing Architectures, such as:
      • Language Understanding Architecture for text comprehension.
      • Language Generation Architecture for text production.
      • Dialogue System Architecture for conversation management.
    • Computer Vision Architectures, such as:
      • Image Recognition Architecture for visual identification.
      • Video Analysis Architecture for motion understanding.
      • Scene Interpretation Architecture for environmental context.
  • AI Application Architectures, such as:
    • Enterprise AI Architectures, such as:
      • Decision Support Architecture for business intelligence.
      • Process Automation Architecture for workflow enhancement.
      • Customer Intelligence Architecture for relationship management.
    • Consumer AI Architectures, such as:
      • Personal Assistant Architecture for individual productivity.
      • Recommendation System Architecture for personalized suggestion.
      • Smart Home Architecture for environmental automation.
  • ...
• Counter-Examples:
  • Traditional Software Architecture, which lacks AI-specific components and model integration.
  • Hardware Architecture, which focuses on physical components rather than AI capabilitys.
  • Enterprise Information Architecture, which addresses organizational data without AI processing.
  • Network Architecture, which emphasizes communication infrastructure over AI functionality.
  • System Implementation, which describes specific instances rather than architectural patterns.
• See: System Architecture Model, AI Framework, Model Architecture, Intelligent System Design, AI Component Model.

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