We talk with Dan Romuald Mbanga, Global Lead of Business Development for Amazon AI, about teaching students to use SageMaker for training and deploying deep learning models.
Deep Learning is one of the most exciting technology fields in the world today, and because Udacity’s learning platform is built to allow for maximum adaptability, our Deep Learning Nanodegree program is one of our most dynamic and future-facing programs right now, as we continue to respond to advances in the field by augmenting and enhancing our curriculum.
We are very excited to share details about the latest additions to our program curriculum, which include new content and projects focused on PyTorch and SageMaker. In a recent post by Cezanne Camacho, Curriculum Lead for Udacity’s School of Artificial Intelligence, we discussed new PyTorch content, and today, we’re going to explore how we’ll be teaching students to use SageMaker for training and deploying deep learning models.
To integrate the incredible new content, we teamed up with AWS and the SageMaker team, and in the updated program, students will train and deploy a sentiment analysis model on SageMaker, then connect it to a front end through an API using other AWS services. After deploying a model, students will also learn how to update their model to account for changes in the underlying data used to train their model—an especially valuable skill in industries that continuously collect user data.
To provide a closer look into the world of SageMaker, we spoke recently with Dan Romuald Mbanga, Global Lead of Business Development for Amazon AI, and a leader of business and technical initiatives for Amazon AI platforms.