AWS IoT services enable you to easily and securely connect and manage billions of devices. You can gather data from, run sophisticated analytics on, and take actions in real-time on your diverse fleet of IoT devices from edge to the cloud. AWS Greengrass is software that lets you run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way.

With Amazon Web Services (AWS), you can spin up EC2 compute capacity on demand with no upfront commitments. You can do this even more cost effectively by using Amazon EC2 Spot Instances to bid on spare Amazon EC2 computing capacity. This allows users to get 90% off on demand prices (often as little as 1c per core hour) and has helped them run very large scale workloads cost effectively. For example, at USC a computational chemist spun up 156,000 core in three days. Also, with the recent release of the Spot fleet API, a researcher or scientist can easily have access to some of the most cost effective compute capacity at a very large scale. Learn how to effectively use these tools for your research needs.

Deriving insights from data is foundational to nearly every organization, and many researches process high volumes of data every day. One common requirement of customers in life sciences is the need to analyze these data in a high-throughput fashion without sacrificing time-to-insight. Such analyses, which tend to be composed of a series of massively parallel processes (MPP) are well suited to the AWS Cloud.

In this session, we will introduce you to AWS and then show you how to set up genomics workflows on AWS. We will also show to users how to optimize Amazon EC2 Spot Instances use and save up to 90% off of traditional On-Demand prices.

Note: this approach to batch processing can be generalized to any type of batch workflow so anyone is welcome to attend.

Amazon SageMaker is a fully- managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. We will show you how to train and build a ML model on SageMaker then how to deploy the inference end points on tools like AWS Greengrass or Serverless applications.

In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes, and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon AI/ML services work together to build a successful data lake for various roles, including data scientists and business users.