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.

未指定特定的檔案、測試或進入點。請檢視連結的 Azure ML 訓練資料，並將其與所描述的 Blob、pipeline、DevOps、ACI 和 AKS 工作流程進行比較。完成條件是為列出的所有步驟提供完整的 SDK V2 指南，包括 issue 指出现有範例中的缺漏。

由索引模型根據 Issue 內容生成。