Azure Machine Learning
As you probably know by following the walkthrough, a machine learning project is composed of a lot of iterative processes, such as getting fresh new data, training different models, finding the right one, testing it, and deploying it into production. It normally requires different functionalities, and data scientists use different technologies and tools to control this pipeline.
The great news is that Azure Machine Learning simplifies this process greatly. By supporting open source technologies and Python packages, such as PyTorch, TensorFlow, and scikit-learn, data scientists like yourself will be able to train models on your machines using your favorite tools, such as Jupyter Notebooks, and then scale out to the cloud. You can then create build clusters programmatically using the Azure Machine Learning SDK for Python, or attach them to existing ones. A lot of different compute targets are available, such as Azure Machine Learning Compute or Azure Databricks, as you...
