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Goal

Implement a working ML batch prediciton use case with Azure

Supported Activities

  • Model Training
  • Scheduled data movement from local db to cloud storage
  • Batch Prediction

TODOs

  • Load training data into storage (x)

  • Register data store (x)

  • Train model & register with MLFlow (x)

  • Create scoring script & create docker image

  • Deploy docker to batch prediction endpoint

  • Azure Data Factory for scheduling data movement from db to storage

  • ADF should trigger batch endpoint

  • automatize as much as possible with pulumi & github actions

Last Status

  • training and logging model to right experiment works
  • Next steps:
    • try in jupyter notebook to retrieve the model and make predictions
    • deploy model to batch endpoint with custom batch_driver

Knowledge Sources: