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.