Microsoft Azure's machine learning platform, ML Studio, serves as a central hub for cloud-based machine learning, from building to utilsing. The platform offers robust, scalable compute with intergrated coding environments that are pre-configured with popular frameworks like TensorFlow, PyTorch, and Scikit-learn.
The platform also includes collaborative features, automated machine learning capabilities, and integrated data management tools, making it particularly valuable for research teams who have many people working on different parts of the pipeline.
Your Learning Path
In this module we’ll try to run through a range of resources available in ML Studio that will appeal to both those with no coding skills and surface level knowledge of machine learning algorithms to those who are adept at creating and running powerful machine learning models on big data. If you feel some sections are not relevant to your research needs, feel free to skip ahead to those that best suit your research.
Modes of Learning
Much of the content within this module will be video tutorials supplimented with text guides. Throughout the module publicily available datasets will be used and in some cases annotated coding notebooks (Jupyter Notebooks). These will all be made available in our Github in the ML Studio folder, see the link to the GitHub repository in the top right, left of the L2D home button.