MCP Agentic AI Training Help

I’m building an Agentic AI workflow on Microsoft’s MCP stack and have reached the model-training phase. The project is entirely Python based, runs in Azure, and works with text data pulled from my existing storage account. I need hands-on guidance and clean, well-documented code that will let me:

• ingest and preprocess the text corpus from Azure Blob or Data Lake
• configure and launch training jobs inside Azure ML (or another MCP-compatible service)
• monitor metrics, handle checkpoints, and push trained weights back to Azure
• keep the whole pipeline modular so I can swap models or scale later

You should already be comfortable with Agentic AI patterns, the MCP orchestration layer, Azure ML SDK, and the usual Python/NLP libraries (Transformers, PyTorch/TensorFlow, MLflow for tracking). Clear comments and step-by-step explanations are essential because I’ll be extending the pipeline myself after delivery.

Acceptance criteria
— end-to-end script or notebook runs in my Azure subscription without edits
— logs training metrics in the workspace and stores the final model artifact
— includes a brief README describing each component and how to rerun or adapt it

If this matches your skill set, let’s get started.