Azure Machine Learning - Data Ingestion

Overview

Data ingestion is a critical step in the Azure Machine Learning workflow. It involves preparing and loading your data into Azure Machine Learning for model training and deployment.

This documentation covers:

• Preparing data for ingestion
• Using data stores
• Ingesting data from various sources
• Best practices for efficient data loading

Data Stores

Azure Machine Learning uses data stores to manage and access your data. Data stores are the primary mechanism for providing your data to your training jobs.

Common data store types include:

• Azure Blob Storage
• Azure Data Lake Storage Gen2
• Azure SQL Database
• Azure Synapse Analytics

Learn more about data stores.

Ingesting Data

You can ingest data into Azure Machine Learning using various methods, depending on your data source and requirements.

• Using the Azure Machine Learning SDK

• Using the Azure Machine Learning command-line interface (CLI)

• Deploying data ingestion jobs

Refer to the official documentation for detailed instructions and examples.