AI Pipeline

An AI Pipeline is a data pipeline that processes data through AI models and AI processing stages to produce AI-generated output.

• AKA: Machine Learning Pipeline, AI Processing Pipeline, Model Pipeline, AI Workflow.
• Context:
  • It can typically ingest Raw Data through data collection interfaces.
  • It can typically preprocess Input Data via data transformation stages.
  • It can typically execute Model Inference using trained models.
  • It can typically postprocess Model Output with output formatting.
  • It can typically manage Pipeline State through orchestration layers.
  • It can typically monitor Pipeline Performance via telemetry systems.
  • ...
  • It can often include Feature Engineering for data preparation.
  • It can often implement Model Ensemble for prediction combination.
  • It can often support Batch Processing for high-volume data.
  • It can often enable Stream Processing for real-time data.
  • It can often provide Error Handling for fault tolerance.
  • ...
  • It can range from being a Simple AI Pipeline to being a Complex AI Pipeline, depending on its stage count.
  • It can range from being a Single-Model AI Pipeline to being a Multi-Model AI Pipeline, depending on its model diversity.
  • It can range from being a Batch AI Pipeline to being a Streaming AI Pipeline, depending on its processing mode.
  • It can range from being a Local AI Pipeline to being a Distributed AI Pipeline, depending on its deployment architecture.
  • It can range from being a Static AI Pipeline to being a Dynamic AI Pipeline, depending on its configuration flexibility.
  • It can range from being a Development AI Pipeline to being a Production AI Pipeline, depending on its deployment maturity.
  • ...
  • It can integrate with Data Storage System for data persistence.
  • It can connect to Model Registry for model versioning.
  • It can utilize Compute Resource for processing power.
  • It can implement Monitoring System for observability.
  • It can employ Security Layer for data protection.
  • ...
• Example(s):
  • Training AI Pipelines, such as:
    • Deep Learning Training Pipeline for neural network training.
    • AutoML Pipeline for automated model selection.
    • Federated Learning Pipeline for distributed training.
  • Inference AI Pipelines, such as:
    • Real-Time Prediction Pipeline for online serving.
    • Batch Scoring Pipeline for offline processing.
    • Edge Inference Pipeline for device deployment.
  • Data Processing AI Pipelines, such as:
    • NLP Pipeline for text processing.
    • Computer Vision Pipeline for image analysis.
    • Audio Processing Pipeline for speech recognition.
  • End-to-End AI Pipelines, such as:
    • MLOps Pipeline for complete lifecycle.
    • AutoML Pipeline for automated workflow.
    • Production ML Pipeline for enterprise deployment.
  • ...
• Counter-Example(s):
  • Traditional Data Pipelines, which lack AI model integration.
  • Manual Analysis Processes, which lack automation.
  • Single Model Execution, which lacks pipeline structure.
  • Static Report Generation, which lacks AI processing.
• See: Data Pipeline, Machine Learning Pipeline, MLOps, Model Deployment, Data Processing, Feature Engineering, Model Serving, Workflow Orchestration, Stream Processing, Batch Processing.

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