E84 @ AWS Public Sector Summit in Washington DC
We’ve been heads down, hard at work tackling challenges in Earth Science and Disaster Response and are ready to share what we’re doing with all of you. June 11-12, we’ll be joining AWS at their Public Sector Summit in Washington, DC to talk about some of these challenges and how we’re working to solve them. […]
Exploring Serverless Portability
With Functions as a Service (FaaS), cloud providers promise us the ability to deploy, run, and scale small pieces of application code without worrying about the underlying hardware or virtual machines. This comes with certain advantages, such as a pricing model that only charges for the amount of time the code is running, quick deployments, […]
Fast, Secure, Reliable Uploads to Amazon Web Services from a Web Browser
The Problem Recently, I needed to implement large file uploads to Amazon Web Services (AWS) from a web browser. We had some requirements with possible solutions: S3 is the ideal place to upload files but there are issues with uploading from a web browser to S3. You don’t want to give users free reign to […]
Decision Making @ The Edge
In July 2017 Amazon Web Services (AWS) made Lambda@Edge available for all customers, giving organizations the ability to run code on the over 100 CloudFront edge locations around the world at the point that is closest to the user. While Lambda@Edge functions are more limited than regular lambda functions, they give developers the ability to […]
High Stakes, Hard Certs
Deciding to take the AWS Certified Solutions Architect: Professional exam at re:Invent. This certification is considered one of the toughest to achieve in IT, and it lived up to the hype. It was brutal. So brutal in fact, that after seeing that I passed, I questioned why I took it, whether it was worth it, and if it was worth the stress taking it in Las Vegas at Re:Invent versus a local testing center.
E84 Lab Notes: Machine Learning with SageMaker
In a previous post we showed how the E84 R&D team used RoboSat by Mapbox to prepare training data, train a machine learning model, and run predictions on new satellite imagery. In this example, we’re going to use the same imagery source and label data as a proxy for data produced by our AWS disaster […]