Amazon SageMaker helps provide the best model performance for less cost. In this session, we walk through a TCO analysis of Amazon SageMaker, exploring its three modules-build, train, and deploy. Learn how Amazon SageMaker automatically configures and optimizes ML frameworks such as TensorFlow, MXNet, and PyTorch, and see how to use pre-built algorithms that are tuned for scale, speed, and accuracy. We explain how the automatic model tuning feature performs hyperparameter optimization by discovering interesting features in your data and learning how those features interact to affect accuracy. Learn how to deploy your model with one click and how to lower inference costs using Amazon Elastic Inference. We end by showing how Aramex uses Amazon SageMaker.