Packages
Client library for uploading, downloading, and managing blobs and containers in Microsoft Azure Blob Storage.
Install it if you need to work with Azure Blob Storage from Python; it is the standard choice for that task.
Provides shared client library code for Microsoft Azure Storage services (blob, queue, file), handling authentication, common operations, and utilities used by service-specific packages.
No, not for new projects.
Provides a Python client library to create, read, update, and delete files and shares in Microsoft Azure Storage, with support for advanced file operations.
However, do not adopt it for new projects—Microsoft has deprecated this package in favor of azure-storage-file-share, which receives active maintenance.
Provides Python client library for Azure Data Lake Storage Gen2, enabling directory-level operations, ACL management, and file system interactions on hierarchical namespace-enabled storage accounts.
Install it if you need to interact with Azure Data Lake Storage from Python—it is the standard choice for this use case.
Interact with Azure File Share storage—create, read, update, and delete files and directories in cloud-hosted SMB file shares accessible from Windows, Linux, and macOS.
Install it if you need to work with Azure File Share storage from Python—it's the standard way to do so.
Provides the Azure Storage namespace package that enables other Azure Storage client libraries (blob, file, queue) to coexist under a shared namespace.
Client library for interacting with Azure Queue Storage to send, receive, and manage messages in cloud queues via HTTP/HTTPS.
Install it if you need to interact with Azure Queue Storage from Python; it is the standard choice for this use case.
Provides Python bindings to manage access control in Azure Synapse workspaces, enabling role and permission administration through the Azure SDK.
Provides Python client library for Azure Synapse Artifacts, enabling programmatic access to create, manage, and interact with Synapse workspace artifacts including pipelines, datasets, and linked services.
Install it if you need programmatic control over Synapse workspace artifacts; skip it if you only use the Azure Portal or CLI for artifact management.
Manages private endpoints for Azure Synapse workspaces, enabling secure network connectivity to Synapse resources without exposing them to the public internet.
Client library for interacting with Apache Spark clusters in Azure Synapse Analytics, providing programmatic access to Spark job submission and monitoring.
However, verify that its API surface and feature set meet your needs before committing to it for new projects, given the gap since the last release.
Provides base infrastructure for Azure Functions Python extensions to recognize and bind to SDK types and HttpV2 types, enabling custom bindings and handlers.
Connects to Azure Machine Learning Studio workspaces to download, upload, and manage datasets from Python, with optional conversion to and from Pandas DataFrames.
Collects model input and output data during Azure Machine Learning inference for monitoring and debugging, logging tabular data to configurable sinks at arbitrary points in a scoring pipeline.
However, the dormant maintenance status (841 days since last release) means you should verify compatibility with your current Azure ML SDK version and expect no…
Provides internal AutoML infrastructure and utilities for the Azure Machine Learning SDK's automated machine learning training pipeline.
No, not for direct installation.
Provides utilities for handling raw HTTP requests in Azure ML webservices, built on Flask.
However, the Alpha status and unclear proprietary license mean you should verify the license terms for your use case and be prepared for potential API changes.
Provides core APIs and utilities for managing Azure Machine Learning workspaces, experiments, compute resources, datasets, models, and training runs.
However, do not start new projects with it—it is deprecated and will receive only security fixes until June 2026.
Loads, transforms, and writes data for Azure Machine Learning workflows, with built-in support for multiple data formats and cloud integration.
Provides native code extensions for the azureml-dataprep package, enabling optimized data preparation operations for Azure Machine Learning workflows.
Install it if azureml-dataprep is a dependency of your project.
A Rust-based execution engine for Azure Machine Learning Data Prep that accelerates data loading, transformation, and writing operations in ML workflows.
Coordinates internal Azure Machine Learning SDK dependencies for dataset operations; not intended for direct use.
azureml-defaults is a metapackage that bundles Azure Machine Learning runtime dependencies for inference and dataset operations.
Provides Python SDK access to Azure ML Feature Store for developing feature sets, managing feature specifications, and running offline feature retrieval with point-in-time joins.
However, the 189-day gap since last release and aging maintenance status suggest slower iteration; verify that the current feature set (including online store…
Provides a filesystem interface to Azure Machine Learning datastores via fsspec, allowing you to access data files using azureml:// URIs with standard data-processing libraries.
Provides an HTTP server for serving Azure Machine Learning models with built-in support for Flask-based scoring scripts, OpenTelemetry observability, and Azure Monitor integration.
However, review the unclear proprietary license terms before use, and ensure your scoring scripts are compatible with Flask 2.x and Pydantic 2.0+.
Integrates MLflow experiment tracking and model management with Azure Machine Learning workspaces, allowing you to log metrics and artifacts to AzureML while using MLflow APIs.
Builds, optimizes, and manages machine learning workflows in Azure by composing data preparation, model training, and deployment steps into executable pipelines that can be submitted and tracked.
Provides core pipeline infrastructure for Azure Machine Learning, enabling definition and execution of configurable machine learning workflows.
Defines step types for building computation units in Azure Machine Learning pipelines, enabling orchestration of training, data processing, and automated ML workflows.
Provides a Python SDK to build, train, and deploy machine learning models on the Azure Machine Learning service, supporting workflows from any Python environment including Jupyter Notebooks and IDEs.
However, clarify the custom license terms first, and verify that the five runtime dependencies align with your use case—this is a heavyweight, cloud-coupled package,…
Collects telemetry data from Python code by logging messages, metrics, events, and activity to Application Insights for monitoring and diagnostics.
A deprecated wrapper package that redirects users to azureml-train-core for Azure Machine Learning training functionality.
Automatically discovers and trains the best machine learning model and hyperparameters for your dataset, handling model selection and tuning without manual experimentation.
Automatically selects and tunes machine learning models given training and test data, handling model search and hyperparameter optimization as part of Azure ML's AutoML workflow.
Provides estimator classes for training machine learning models in Azure Machine Learning, including support for deep neural networks, hyperparameter tuning, and Scikit-Learn workflows.
However, it is tightly coupled to Azure ML's ecosystem—it is not a standalone training framework.
Provides REST client classes for creating and managing HyperDrive hyperparameter tuning runs within the Azure ML SDK ecosystem.
However, clarify whether you need it directly or if a higher-level Azure ML package already includes it as a dependency—it is a specialized component, not a…