AI-System Software Architecture Layer
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An AI-System Software Architecture Layer is a software architecture layer in an AI-system software architecture that organizes AI system components (to support artificial intelligence capabilitys through AI layer organization).
- AKA: AI Architecture Layer.
- Context:
- 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 Layer to being a Production AI Layer, depending on its deployment context.
- It can range from being a Single Model Layer to being a Multi Model Layer, depending on its model architecture.
- It can range from being a Batch Processing Layer to being a Real-Time Processing Layer, depending on its inference pattern.
- ...
- It can integrate with Enterprise Software Layer for business systems.
- It can connect to Cloud Software Layer for infrastructure systems.
- It can utilize Data Software Layer for storage systems.
- ...
- Examples:
- AI Core Layers, such as:
- AI Model Software Layers for AI model systems, such as:
- Model Development Layers for model creation systems, such as:
- Training Pipeline Layer for model training systems, such as:
- LLM Layers for language model systems, such as:
- Model Configuration Layers for model setup systems, such as:
- Model Development Layers for model creation systems, such as:
- AI Agent Software Layers for AI agent systems, such as:
- Agent Framework Layers for agent development systems, such as:
- Agent Core Layer for agent logic systems, such as:
- Agent Interaction Layer for agent communication systems, such as:
- Agent Memory Layer for agent state systems, such as:
- Agent Framework Layers for agent development systems, such as:
- AI Data Software Layers for AI data systems, such as:
- Data Pipeline Layers for data processing systems, such as:
- Data Collection Layer for data ingestion systems, such as:
- Model Configuration Data Layer for model parameter systems, such as:
- Data Pipeline Layers for data processing systems, such as:
- AI Service Software Layers for AI service systems, such as:
- Model Serving Layers for inference systems, such as:
- Inference Service Layer for prediction systems, such as:
- Service Management Layer for operation systems, such as:
- Service Monitoring Layer for observability systems, such as:
- Model Serving Layers for inference systems, such as:
- ...
- AI Model Software Layers for AI model systems, such as:
- AI Supporting Layers, such as:
- AI Application Layers, such as:
- ...
- AI Core Layers, such as:
- Counter-Examples:
- Traditional Software Layer Group, which lacks AI-specific capabilitys.
- Data Platform Layer Group, which focuses on data management rather than AI processing.
- Analytics Layer Group, which handles business intelligence rather than machine learning.
- See: AI Architecture Pattern, ML System Design, AI Platform Architecture, Model Development Lifecycle.