Developers spend a lot of time and effort to deliver accurate machine learning models that can make fast, low-latency predictions in real-time. This is particularly important for edge devices where memory and processing power tend to be highly constrained, but latency is very important. Amazon SageMaker Neo automatically optimizes machine learning models. You start with a machine learning model built using MXNet, TensorFlow, PyTorch, or XGBoost and trained using Amazon SageMaker. Then you choose your target hardware platform from Intel, NVIDIA, or ARM. With a single click, SageMaker Neo will then compile the trained model into an executable. The compiler uses a neural network to discover and apply all of the specific performance optimizations that will make your model run most efficiently on the target hardware platform. The model can then be deployed to start making predictions in the cloud or at the edge.