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Azure ML Pipeline with V2 SDK

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
20/100
Issue type
Documentation
Clarity
Needs clarification
Activity status
Stale
Tech stack
azure, jupyter-notebook, kubernetes

Research direction

No specific file, test, or entry point is identified. Review the linked Azure ML training material and compare it with the described Blob, pipeline, DevOps, ACI, and AKS workflow. Done would be complete SDK V2 guidance for all listed steps, including the gaps the issue reports in existing examples.

Written by the indexing model from the issue text.

Description

Previously microsoft suggested us to learn Azure ML Pipeline (using SDK V1) & then use it for our model creation. Link for the training is https://learn.microsoft.com/en-us/training/paths/build-ai-solutions-with-azure-ml-service/.

The process that we followed:

  1. Azure Data Factory generates a new file every month for all the models & publishes in Blob storage.
  2. We have created and published Azure ML Pipeline for models which gets data from Blob, preprocess the data, train model, register the model, finds feature importance, finds data drift.
  3. Then we have used the Azure ML Pipeline ID in DevOps build pipeline, so that the ML Pipeline can be triggered through build Pipeline (The build pipeline gets triggered through Azure Logic App, whenever a new file is published to the Blob container).
  4. Post DevOps pipeline's completion, release pipeline deploys the model into ACI & AKS.

Now we have been suggested to use SDK V2 for all our model training & other processes.
Please suggest, how can we perform all the above steps using SDK V2. All the microsoft documents are incomplete to answer this.
As SDK V1 is legacy now, we are bound to move our code to V2. But SDK V2 examples are incomplete to address our issues.

Dominant language
Jupyter Notebook
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Forks
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