机器学习系统设计模式

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

• Edge prediction pattern: To do

• 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

• Date 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

• Antipatterns -　Todo

github地址：https://github.com/mercari/ml-system-design-pattern