Amazon Web Services Training

You can select one or both of the following training options:

1. AWS for Research Training: Learn about AWS at an all-day classroom training with demonstrations and a hands-on lab.
2. Explore Your Project on AWS: Bring your project or use case for a 45-minute One-on-One session with an AWS architect. You can sign up as an individual or as a team.

Draft Agenda

8:30 – 9:00 – Welcome and Introductions
9:00 – 10:00 – AWS Fundamentals Recap: We’ll begin the day with a recap of AWS services. What is AWS and what sets us apart? We will review the AWS Global Infrastructure and get an overview of AWS services. Finally, we will talk about security and compliance in the cloud.
10:00 – 11:00 – High Performance Computing (HPC): We’ll see how researchers use AWS to run scientific computing workloads, including high-throughput as well as tightly-coupled MPI jobs, for weather forecasting, modeling of materials, genomics sequencing, and more. We’ll explain how to set up flexible compute clusters on Amazon EC2 and will compare “cloud clusters” to on-premise clusters.
11:00 – 11:15 – Break
11:15 – 12:15 – Research Computing with Containers: Containers are increasingly used in research for the sake of easy deployment and portability. We’ll see examples of how researchers use containers on AWS including Kubernetes, and we’ll dive deeper on Batch computing and workflow management.
12:15 – 1:30 – Lunch and Hands on Computing Lab: either Alces Flight compute cluster or Batch computing
1:30 – 2:30 – Big Data and Analytics: In this session we will discuss how Big Data is evolving and why customers are choosing AWS for their analytic solutions. We will look at the AWS services for analyzing Big Data and discuss which tools are best to solve various Big Data problems.
2:30 – 2:45 – Break
2:45 – 3:45 – AI and Deep Learning: After a brief overview of the ML services and activities at AWS, we dive deeper into the Amazon SageMaker service: a managed ML service that places the power of ML in the cloud — including powerful parallel GPU training clusters, preoptimized training algorithms, and scaling ML model end points — in the hands of every scientist who is comfortable working in a simple Jupyter notebook.
3:45 – 4:45 – Hands on ML Lab: We will bring several SageMaker notebooks for you to work through, from basic TensorFlow tasks to genomics examples
4:45 – 5:00 – Wrap up and Questions