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Azure ML Pipeline with V2 SDK

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评估

难度
5/5
预计耗时
一周以上
新手友好度
20/100
Issue 类型
文档
描述清晰度
需要澄清
活跃度
停滞
技术栈
azure, jupyter-notebook, kubernetes
领域
cloud, data-engineering, devops, machine-learning

调研方向

未指定具体的文件、测试或入口点。请查看链接的 Azure ML 培训材料，并将其与所描述的 Blob、pipeline、DevOps、ACI 和 AKS 工作流进行比较。完成标准是为列出的所有步骤提供完整的 SDK V2 指南，包括 issue 指出现有示例中的缺失部分。

由索引模型根据 Issue 内容生成。

描述

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.

主要语言
Jupyter Notebook
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