AI System Configuration

An AI System Configuration is a comprehensive validated software system configuration that specifies the complete operational parameters, architectural choices, and capability settings for an AI system deployment.

• AKA: AI System Setup, AI System Parameter Configuration, AI Deployment Configuration.
• Context:
  • It can typically define AI System Behavior through parameter values, threshold settings, and mode selections.
  • It can typically specify AI System Architecture via component selections, module arrangements, and interface definitions.
  • It can typically establish AI System Capability through feature enablements, function activations, and service configurations.
  • It can typically determine AI System Performance via optimization settings, resource allocations, and scaling parameters.
  • It can typically enforce AI System Constraints through limit specifications, boundary definitions, and rule configurations.
  • ...
  • It can often require Configuration Validation for consistency checking and constraint satisfaction.
  • It can often support Configuration Versioning through change tracking and rollback capability.
  • It can often enable Configuration Composition via modular settings and hierarchical structures.
  • It can often facilitate Configuration Migration between environments and deployment stages.
  • ...
  • It can range from being a Minimal AI System Configuration to being a Comprehensive AI System Configuration, depending on its parameter coverage.
  • It can range from being a Static AI System Configuration to being a Dynamic AI System Configuration, depending on its runtime adaptability.
  • It can range from being a Default AI System Configuration to being a Customized AI System Configuration, depending on its specialization degree.
  • It can range from being a Development AI System Configuration to being a Production AI System Configuration, depending on its deployment maturity.
  • It can range from being a Single-Instance AI System Configuration to being a Distributed AI System Configuration, depending on its deployment topology.
  • ...
  • It can integrate with AI System Configuration Spaces for valid configuration identification.
  • It can interface with AI System Deployment Platforms for configuration application.
  • It can connect to AI System Monitoring Frameworks for configuration tracking.
  • It can communicate with AI System Governance Constraints for compliance verification.
  • ...
• Example(s):
  • Model AI System Configurations, such as:
    • Neural Network Configuration, specifying layer architecture, activation functions, and hyperparameters.
    • Language Model Configuration, defining context window, temperature settings, and token limits.
  • Deployment AI System Configurations, such as:
    • Cloud AI System Configuration, setting instance types, scaling policys, and network parameters.
    • Edge AI System Configuration, specifying resource constraints, optimization levels, and hardware mappings.
  • Safety AI System Configurations, such as:
    • Sandbox AI System Configuration, enforcing isolation boundaryes and access restrictions.
    • Production Safety Configuration, implementing fail-safe mechanisms and monitoring thresholds.
  • Domain-Specific AI System Configurations, such as:
    • Medical AI System Configuration, including regulatory compliance settings and patient safety parameters.
    • Financial AI System Configuration, specifying risk limits and trading constraints.
  • ...
• Counter-Example(s):
  • AI System Architecture, which defines structural design rather than parameter settings.
  • AI System Code, which implements functionality rather than configuration.
  • AI System Documentation, which describes system information rather than operational settings.
• See: AI System Configuration Space, Software System Configuration, System Parameter, Configuration Management, Deployment Setting, System State, AI System Architecture.

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