机器学习系统设计模式

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摘要: 关于机器学习设计模式的课

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入口

Serving patterns

The serving patterns are a series of system designs for using machine learning models in production workflow.

  • Web single pattern
  • Synchronous pattern
  • Asynchronous pattern
  • Batch pattern
  • Prep-pred pattern
  • Microservice vertical pattern
  • Microservice horizontal pattern
  • Prediction cache pattern
  • Data cache pattern
  • Prediction circuit break pattern
  • Multiple stage prediction pattern
  • Serving template pattern
  • Edge prediction pattern
  • Antipatterns
    • Online bigsize pattern
    • All-in-one pattern

QA patterns

Pattens to evaluate model as well as prediction server.

  • Shadow AB-testing pattern
  • Online AB-testing pattern
  • Loading test pattern
  • Antipatterns
    • Offline-only pattern

Training patterns

Patterns to construct training pipeline.

  • Batch training pattern
  • Pipeline training pattern
  • Parameter and architecture search pattern
  • Antipatterns
    • Only-me pattern
    • Training code in serving pattern
    • Too many pipes pattern

Operation patterns

The operation patterns contain configuration, logging, monitoring and alerting system designs for machine learning system.

  • Model-in-image pattern
  • Model-load pattern
  • Data model versioning pattern
  • Prediction log pattern
  • Prediction monitoring pattern
  • Parameter-based serving pattern
  • Condition-based-serving pattern
  • Antipatterns
    • No logging pattern
    • Nobody knows pattern

Lifecycle patterns

The lifecycle patterns contain composition of several patterns to realize actual ML system with operation.

  • Train-then-serve pattern
  • Training-to-serving pattern

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