Key areas of the SDK include: * Explore, prepare and manage the lifecycle of your datasets used in machine learning experiments. * Manage cloud resources for monitoring, logging, and organizing your machine learning experiments. * Train models either locally or by using cloud resources, including GPU-accelerated model training. * Use automated machine learning, which accepts configuration parameters and training data. It automatically iterates through algorithms and hyperparameter settings to find the best model for running predictions. * Deploy web services to convert your trained models into RESTful services that can be consumed in any application.