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Register and Share a Dataset and Environment
You are a Data Scientist at Globomantics, tasked with registering reusable assets in Azure Machine Learning so the team can run experiments consistently. Your goal is to define a custom environment with the required training packages, upload a solar panel telemetry dataset, and tag both the environment and dataset for governance and traceability.
Lab Info
Table of Contents
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Challenge
Register a custom environment with training dependencies
- Create a custom environment in Azure ML that includes scikit-learn and PyTorch as pip dependencies.
- Apply metadata tags to the environment to capture project name, framework versions, Python version, and intended use case.
- Verify the environment builds successfully and appears in the Azure ML Studio.
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Challenge
Upload and register a dataset with metadata
- Upload the provided solar_panel_telemetry.csv (2.1 MB) as a dataset into Azure ML Studio.
- Add metadata tags to the dataset to record the project name, data source, and target column.
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