AI-Centric System Architecture

An AI-Centric System Architecture is a software system architecture for AI-driven systems (uses artificial intelligence as core processing components).

AKA: AI System Architecture, Intelligent System Architecture, AI-Powered System Architecture.
Context:
- It can (typically) process AI-centric system input through AI-centric system processing components.
- It can (typically) manage AI-centric system state through AI-centric system state management mechanisms.
- It can (typically) handle AI-centric system integration through AI-centric system interfaces.
- It can (typically) generate AI-centric system output through AI model inference and AI output formatting.
- It can (typically) perform AI-centric system reasoning through AI model chain and AI orchestration workflows.
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- It can (often) evaluate AI-centric system quality through AI model performance metrics and AI output validation.
- It can (often) adapt AI-centric system behavior through AI feedback loops and AI learning mechanisms.
- It can (often) implement AI-centric system recommendation through recommender models and personalization algorithms.
- It can (often) ensure AI-centric system robustness through AI fallback mechanisms and AI error handling.
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- It can range from being a Basic AI-Centric System Architecture to being an Advanced AI-Centric System Architecture, depending on its AI-centric system processing capability.
- It can range from being a Single Domain AI-Centric System Architecture to being a Multi-Domain AI-Centric System Architecture, depending on its AI-centric system application scope.
- It can range from being a Standalone AI-Centric System Architecture to being a Distributed AI-Centric System Architecture, depending on its AI-centric system deployment model.
- It can range from being a Supervised AI-Centric System Architecture to being a Self-Learning AI-Centric System Architecture, depending on its AI-centric system adaptation capability.
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- It can (typically) integrate with AI-centric system data storage for AI-centric system information management.
- It can (typically) connect to AI-centric system model repository for AI-centric system model deployment.
- It can (typically) support AI-centric system monitoring for AI-centric system performance tracking.
- It can (often) incorporate AI-centric system telemetry collection for AI-centric system usage analysis.
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Examples:
- AI-Centric System Processing Type Architectures, such as:
  - Language Processing AI-Centric System Architectures, demonstrating AI-centric system processing capabilities, such as:
    - LLM-Centric System Architecture for language model processing, implementing AI-centric system reasoning through transformer models.
    - NLP System Architecture for text analysis, managing AI-centric system state through language processing pipelines.
  - Vision Processing AI-Centric System Architectures, processing AI-centric system input through neural networks, such as:
    - Computer Vision System Architecture for image analysis, generating AI-centric system output through visual recognition.
    - Video Processing System Architecture for motion analysis, integrating with AI-centric system data storage for temporal data.
  - Multi-Modal AI-Centric System Architectures, handling AI-centric system integration across modalities, such as:
    - Vision-Language System Architecture for cross-modal processing, implementing AI-centric system recommendation through multimodal understanding.
    - Audio-Visual System Architecture for multimedia analysis, adapting AI-centric system behavior through cross-modal learning.
- Recommender AI-Centric System Architectures, implementing AI-centric system recommendation, such as:
  - Collaborative Filtering System Architecture for user preference modeling, connecting to AI-centric system model repository for recommendation models.
  - Content-Based Recommendation System Architecture for item feature analysis, processing AI-centric system input through similarity metrics.
  - Hybrid Recommender System Architecture for multi-strategy recommendation, evaluating AI-centric system quality through relevance metrics.
- AI-Centric System Complexity Architectures, such as:
  - Basic AI-Centric System Architectures, demonstrating fundamental AI-centric system processing capability, such as:
    - Single Model System Architecture for basic inference, handling AI-centric system integration through simple interfaces.
    - Pipeline System Architecture for sequential processing, managing AI-centric system state through linear workflows.
  - Advanced AI-Centric System Architectures, implementing sophisticated AI-centric system reasoning, such as:
    - Multi-Model System Architecture for ensemble processing, ensuring AI-centric system robustness through model diversity.
    - Hybrid System Architecture for mixed processing, adapting AI-centric system behavior through complementary approaches.
- AI-Centric System Deployment Architectures, such as:
  - Local AI-Centric System Architectures, optimizing for AI-centric system deployment model, such as:
    - Edge AI System Architecture for device processing, supporting AI-centric system monitoring with limited resources.
    - Embedded AI System Architecture for hardware integration, managing AI-centric system state with constrained memory.
  - Cloud AI-Centric System Architectures, scaling AI-centric system processing capability, such as:
    - Amazon Integration System Architecture for AWS deployment, connecting to AI-centric system model repository through cloud services.
    - Microsoft Integration System Architecture for Azure deployment, implementing AI-centric system recommendation at scale.
- AI-Centric System Scale Architectures, such as:
  - Enterprise AI-Centric System Architectures, handling enterprise-level AI-centric system integration, such as:
    - Global Distribution System Architecture for worldwide operation, supporting AI-centric system monitoring across regions.
    - High Availability System Architecture for mission critical tasks, ensuring AI-centric system robustness through redundancy.
  - Research AI-Centric System Architectures, enabling AI-centric system adaptation capability, such as:
    - Experimental System Architecture for model development, evaluating AI-centric system quality for novel approaches.
    - Prototype System Architecture for concept validation, processing AI-centric system input in controlled environments.
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Counter-Examples:
- Traditional System Architecture, which lacks AI-centric system processing capability and relies on fixed programmatic logic.
- Data Pipeline System Architecture, which lacks AI-centric system intelligent processing and focuses solely on data movement.
- Basic Service System Architecture, which lacks AI-centric system integration capabilities required for model orchestration.
- Static Processing System Architecture, which lacks AI-centric system learning capability and cannot adapt to new patterns.
See: Distributed System Architecture, Cloud System Architecture, ML System Architecture, LLM-Centric System Architecture, Neural System Architecture, Recommender System, Software System Architecture, AI Model Deployment Architecture.