Azure Machine Learning can be used for any kind of machine learning, from classical ml to deep learning, supervised, and unsupervised learning. It can be used with Python or R code with the SDK or work with no-code/low-code options in the studio. One can build, train, and track machine learning and deep-learning models in an Azure Machine Learning Workspace.
Azure Machine Learning provides all the tools developers and data scientists need for their machine learning workflows, including:
- The Azure Machine Learning designer: drag-n-drop modules to build your experiments and then deploy pipelines.
- Jupyter notebooks: use our example notebooks or create your own notebooks to leverage our SDK for Python samples for your machine learning.
- R scripts or notebooks in which you use the SDK for R to write your own code, or use the R modules in the designer.
- The Many Models Solution Accelerator (preview) builds on Azure Machine Learning and enables you to train, operate, and manage hundreds or even thousands of machine learning models.
- Machine learning extension for Visual Studio Code users
- Machine learning CLI
- Open-source frameworks such as PyTorch, TensorFlow, and scikit-learn and many more
- Reinforcement learning with Ray RLlib