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:

• https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/scikit-learn/iris/iris-scikit-learn.ipynb
• https://free-braindumps.com/microsoft/free-dp-100-braindumps.html?p=2