Feb 22: AWS Scientific Computing Immersion Day 1Location: TA 117 | Directions | Add To Calendar
UCF will host an AWS Scientific Computing Immersion Day where AWS specialists will conduct training sessions for faculty, post-doc and student researchers interested in computational science and engineering as well as big data analytics. Through a series of hands-on BYOD sessions, researchers will be able to explore the potential use of AWS technologies to advance their research interests.
|9:00 AM - 11:30 AM||Session 1: Introduction to AWS
This introductory session provides a broad overview of Amazon Web Services. It also discusses cloud computing in general and various services in particular that AWS provides for research communities. Participants will be able to activate their AWS credit code and explore the AWS portal for compute and data services. This session will also provide various tools that can be used to manage AWS resources.
|11:30 AM - 1:00 PM||Lunch break|
|1:00 PM - 2:45 PM||Session 2: High Performance Computing
One of the benefits of cloud computing is that with a few clicks, one can create a massive parallel processing system for any scientific workflow. In this session, participants will deploy a cloud formation cluster of linux servers and use it to perform parallel processing. In addition, AWS Batch will be introduced. This enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters allowing researchers to focus on analyzing results and solving problems.
|2:45 PM - 3:00 PM||Break|
|3:00 PM - 5:00 PM||Session 3: AWS Machine Learning
Amazon Machine Learning is a powerful tool for performing predictive analytics on large volumes of data. In this session, participants will use the interactive Amazon Machine Learning tool to build, train, and score models. Additionally, participants will be able to use Amazon Rekognition for large-scale image analysis using deep learning techniques and Amazon Polly to turn text into lifelike speech. Participants will also be able to create applications that talk, enabling them to build various categories of speech-enabled applications. The MXNet based deep learning framework will also be outlined.
For registration questions, please email Laury Anthony (Laury.Anthony@ucf.edu).