Azure ML Pipeline with V2 SDK

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.

Aucun fichier, test ou point d’entrée spécifique n’est identifié. Examinez le matériel de formation Azure ML lié et comparez-le au workflow décrit avec Blob, pipeline, DevOps, ACI et AKS. Ce sera terminé lorsqu’une documentation complète de SDK V2 couvrira toutes les étapes listées, y compris les lacunes signalées par l’issue dans les exemples existants.

Rédigé par le modèle d'indexation à partir du texte de l'issue.