Think Different

  unplash Here are some of the key differences between AWS SageMaker and Azure Machine Learning Studio: AWS SageMaker SageMaker pricing is based on the number of compute hours used, the amount of data stored, and the number of requests made. SageMaker is a fully managed service, which means that Amazon takes care of all the underlying infrastructure. This makes it easy to get started with machine learning, but it also limits your flexibility. SageMaker offers a wide range of features, including: Data preparation and preprocessing Model training and tuning Model deployment and monitoring | Azure Machine Learning Studio offers a similar range of features, but it also includes: A drag-and-drop interface for creating machine learning models A visual workspace for managing your machine learning projects Integration with other Azure services Here is an example of how to use AWS SageMaker to train a machine learning model: import sagemake # Create a SageMaker session. session = sagemaker....