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. We end by showing how Aramex uses Amazon SageMaker.

Inawisdom was engaged by Aramex, a global provider of logistics and transportation solutions, to support a digital transformation by enhancing the customer experience and digitizing the end-to-end shipment journey. "We are shifting increasingly to be an e-commerce company, and our vision is to be an innovative e-commerce provider that provides a revolutionary customer experience," says Mohammed Sleeq, chief digital officer at Aramex. "We wanted to give our customers a more accurate, instant prediction of delivery time, and we knew Inawisdom and AWS would enable us to do what we wanted to accomplish." To realize its transformation goals, Aramex chose Inawisdom as a partner to accelerate the delivery of an AWS cloud-native data science platform to deploy machine learning models into production. Inawisdom helped Aramex deploy the RAMP platform, which takes advantage of Amazon SageMaker and other key AWS services.