机器学习系统设计模式

摘要: 关于机器学习设计模式的课

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入口

• github

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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