Currently, Spark provides status monitoring for different components of Spark, like spark history server, streaming listener, sql listener and etc. The use case would be (1) front UI to track the status of training coverage rate during iteration, then DS can understand how the job converge when training, like K-means, Logistic and other linear regression model. (2) tracking the data lineage for the input and output of training data.
In this proposal, we hope to provide Spark ML pipeline listener to track the status of Spark ML pipeline status includes:
- ML pipeline create and saved
- ML pipeline model created, saved and load
- ML model training status monitoring