Model Deployment System

A Model Deployment System is a deployment system that can deploy machine learning models into production environments for model inference tasks.

• AKA: ML Model Deployment System, AI Model Deployment System, Model Serving System.
• Context:
  • It can typically package Model Deployment Artifact through model deployment containerizers.
  • It can typically serve Model Deployment Endpoint through model deployment API servers.
  • It can typically manage Model Deployment Version through model deployment version controllers.
  • It can typically monitor Model Deployment Performance through model deployment metric trackers.
  • It can typically scale Model Deployment Instance through model deployment auto-scalers.
  • ...
  • It can often validate Model Deployment Input through model deployment input validators.
  • It can often cache Model Deployment Response through model deployment cache layers.
  • It can often balance Model Deployment Load through model deployment load balancers.
  • It can often rollback Model Deployment Update through model deployment rollback mechanisms.
  • ...
  • It can range from being a Simple Model Deployment System to being a Complex Model Deployment System, depending on its model deployment system feature richness.
  • It can range from being a Batch Model Deployment System to being a Real-Time Model Deployment System, depending on its model deployment system latency requirement.
  • It can range from being a Single-Model Deployment System to being a Multi-Model Deployment System, depending on its model deployment system model capacity.
  • It can range from being an Edge Model Deployment System to being a Cloud Model Deployment System, depending on its model deployment system infrastructure location.
  • ...
  • It can integrate with Model Registry for model deployment artifact retrieval.
  • It can connect to Container Platform for model deployment container orchestration.
  • It can interface with API Gateway for model deployment request routing.
  • It can synchronize with Monitoring System for model deployment observability.
  • It can communicate with A/B Testing Platform for model deployment experiment management.
  • ...
• Example(s):
  • Cloud Model Deployment Systems, such as:
    • Amazon SageMaker Endpoint for model deployment AWS hosting.
    • Google Vertex AI Prediction for model deployment GCP serving.
    • Azure ML Endpoint for model deployment Azure platform.
  • Open Source Model Deployment Systems, such as:
    • TensorFlow Serving for model deployment TensorFlow models.
    • TorchServe for model deployment PyTorch models.
    • MLflow Models for model deployment framework-agnostic.
  • Specialized Model Deployment Systems, such as:
    • NVIDIA Triton Server for model deployment GPU inference.
    • Seldon Core for model deployment Kubernetes native.
    • BentoML for model deployment Python-first.
  • ...
• Counter-Example(s):
  • Model Training System, which focuses on model development rather than deployment.
  • Data Pipeline System, which processes data rather than serves models.
  • Application Server, which lacks ML-specific capability.
• See: Machine Learning Platform, Model Serving Infrastructure, MLOps System, Inference Engine, API Platform, Container Orchestration System, Production ML System.

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