Duration
1 day
Overview
To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
Introduction
- Understand URIs
- Create a datastore
- Create a data asset
- Exercise - Make data available in Azure Machine Learning
Summary
In this module, you've learned how to:
- Access data by using URIs.
- Connect to cloud data sources with datastores.
- Use data asset to access specific files or folders.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.