Azure Machine Learning (ML) Studio provides accessible tools for training and deploying machine learning models. The platform was built for users of all levels, with a no code workflow for those unsure where to begin to a dedicated system for submitting large ML training jobs to the Azure computing cluster.
For those with coding experience, we'll demonstrate using the popular ML framework PyTorch with the cloud scalability of Azure ML.
For those with no-coding experience we'll run through the use of Automated ML, a user interface that guides you through selecting the right criteria for predicting on your dataset, both classifcaiton and regression. Once set to run, the Automated ML system will test over 100 ML algorithms against you dataset to find the best performing one.
For Machine Learning Studio, AI Services, and AI Foundry we will be using some general data to work through as examples. Howwever, for the best learning experience try it out with your own data if possible.