Data scientists and machine learning engineers use a variety of open-source projects in their everyday tasks: scikit-learn, SparkML, TensorFlow, Apache MXNet, Pytorch, etc. They make it very easy to get started, but as models become more complex and datasets become larger, training time and prediction latency become a significant concern. Here too, containers can help, especially when used with elastic on-demand compute services. In this session, we'll show you how to scale machine learning workloads using containers on AWS (Deep Learning AMI and containers, ECS, EKS, SageMaker). We'll discuss the pros and cons of these different services from a technical, operational, and cost perspective. Of course, we'll run some demos.
Overview and Author Bio
Scaling your ML workloads from 0 to millions of users
Principal Technical Evangelist at Amazon