Azure Machine Learning
Give your data scientists and developers a wide range of productive experiences to build, train and deploy machine learning models that will improve team collaboration. Reduce time to market with industry-leading Machine Learning Operations or DevOps. Build on a secure platform designed especially for safe and secure machine learning with Azure Machine Learning.
Machine Learning regardless of Skill Level
Jupyter Notebooks and the Drag-and-drop designer make it easy for developers of all skill levels to produce automated machine learning.
MLOps from start to finish
Create and deploy models at scale with automated and reusable machine learning workflows
Machine Learning that lets you Innovate Responsibly
With a wide range of built-in capabilities that allow you to understand, protect and control your data, models and process in a responsible way.
Open, exchange, and utilize
Open source frameworks enjoy the best in class support. Frameworks and languages covered include: MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.
Improve your productivity with easy to use Machine Learning
Build and deploy models quickly regardless of your skill level. Easily use Jupyter Notebooks with IntelliSense or the drag and drop designer.
Speed up your model creation with and access to powerful feature engineering, algorithm selection, and hyperparameter-sweeping capabilities.
Increase your teams efficiency shared datasets, models, notebooks and dashboards that keep track of all the of the aspects of machine learning.
Put your MLOps in operation at scale
Build, deploy and manage models with powerful MLOps to streamline the machine learning cycle
Make Reusable workflows, train, validate and deploy thousands of models at scale with machine learning pipelines.
Managed online batch endpoints allow you to deploy and score models without managing the infrastructure
Manage, and automate your pipelines, and use advanced data-drift analysis to improve model performance over time with Azure DevOps or GitHub Actions.
Machine learning solutions the responsible way
State of the art machine learning capabilities are at your disposal to understand, control, and help protect your data models and processes.
Better describe your models behavior while training and inferencing. Build with fairness in mind by detecting and reducing model bias.
Ensure your data remains private during the MI lifecycle with differential privacy techniques and confidential computing to secure your assets.
Maintain audit trails, track lineage and use model datasheets to enable accountability, automatically.
An innovative, open and flexible platform
Enjoy built-in support for open-source tools and frameworks for MI learning and model inferencing.
Frameworks like Pytorch, TensorFlow or sikit-learn are available
Make use of popular IDE’s, programming languages and other development tools such as Visual Studio Code, Python or Jupyter for development
Track all aspects of your MI training experiments with MLflow
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