The Azure Machine Learning provides a cloud-based environment you can use to develop, train, test, deploy, manage, and track machine learning models. It fully supports open-source technologies, so you can use tens of thousands of open-source Python packages with machine learning components such as TensorFlow and scikit-learn. Rich tools, such as Jupyter notebooks or the Visual Studio Code Tools for AI, make it easy to interactively explore data, transform it, and then develop, and test models. Azure Machine Learning service also includes features that automate model generation and tuning to help you create models with ease, efficiency, and accuracy.
With Azure Machine Learning, you can:
Create a process that defines how to obtain data, how to handle missing or bad data, how to split the data into either a training set or test set, and deliver the data to the training process.
Train and evaluate predictive models by using tools and programming languages familiar to data scientists.
Create pipelines that define where and when to run the compute-intensive experiments that are required to score the algorithms based on the training and test data.
Deploy the best-performing algorithm as an API to an endpoint so it can be consumed in real time by other applications.