Python packages for Azure Machine Learning

Learn about the the proprietary Python packages from Microsoft for Azure Machine Learning. You can use these libraries and functions in combination with other open-source or third-party packages, but to use the proprietary packages, your Python code must run against a service or on a computer that provides the interpreters.

The Azure Machine Learning packages are Python pip-installable extensions for Azure Machine Learning that enable data scientists and AI developers to quickly build and deploy highly accurate machine learning and deep learning models for various domains.

Azure ML Package for Computer Vision

With Azure ML Package for Computer Vision, you can build, fine-tune, and deploy deep learning models for image classification, object detection, and image similarity.

Try these next steps for this package:

  1. Download the package.
  2. Read the install docs.
  3. Build and deploy a computer vision model with a Jupyter notebook.
  4. Explore the package reference documentation.

Azure ML Package for Forecasting

With Azure ML Package for Forecasting, you can create and deploy time series forecasting models for financial and demand forecasting scenarios.

Try these next steps for this package:

  1. Download the package.
  2. Read the install docs.
  3. Build and deploy a forecast model with a Jupyter notebook.
  4. Explore the package reference documentation.

Azure ML Package for Text Analytics

With Azure ML Package for Text Analytics, you can build text deep-learning models for text classification, custom entity extraction, and word embedding.

Try these next steps for this package:

  1. Download the package.
  2. Read the install docs.
  3. Build and deploy a text classification model with a Jupyter notebook.
  4. Explore the package reference documentation.

amlrealtimeai (FPGA acceleration)

With Azure Machine Learning Hardware Acceleration package, data scientists and AI developers can featurize images with a quantized version of ResNet 50, train classifiers based on those features, and then deploy models to an field programmable gate arrays (FPGA) on Azure for ultra-low latency inferencing.

Try these next steps for this package:

  1. Download the package.
  2. Read the install docs.
  3. Deploy a model to an FPGA.