The next evolution of AI and ML is cloud-native, managed platforms and custom hardware AI, inclusive of things like custom AI chips, Internet of Things devices, and more. AWS has Sagemaker, which allows for a fully-managed build, train and deployment lifecycle, including automatic hyperparameter tuning. Microsoft Azure has Azure ML Studio which includes high-level tools allow for drag and drop workspace machine learning workflows. Google has AutoML, which allows developers with limited machine learning expertise to train high quality models, by automatically inferring the correct hyperparameters and model to use. In this training, learn to how to use these managed platforms to create solutions in a fraction of the time as a “roll your own” ML solution. Additionally, learn to compare how each cloud managed solution compares and be able to pick the right solution for the task at hand.