AWS Compute Blog
Category: Artificial Intelligence
Optimizing GPU utilization for AI/ML workloads on Amazon EC2
This blog post is written by Ben Minahan, DevOps Consultant, and Amir Sotoodeh, Machine Learning Engineer. Machine learning workloads can be costly, and artificial intelligence/machine learning (AI/ML) teams can have a difficult time tracking and maintaining efficient resource utilization. ML workloads often utilize GPUs extensively, so typical application performance metrics such as CPU, memory, and […]
Introducing Amazon CodeWhisperer in the AWS Lambda console (In preview)
This blog post is written by Mark Richman, Senior Solutions Architect. Today, AWS is launching a new capability to integrate the Amazon CodeWhisperer experience with the AWS Lambda console code editor. Amazon CodeWhisperer is a machine learning (ML)–powered service that helps improve developer productivity. It generates code recommendations based on their code comments written in […]
Building a low-code speech “you know” counter using AWS Step Functions
This post is written by Doug Toppin, Software Development Engineer, and Kishore Dhamodaran, Solutions Architect. In public speaking, filler phrases can distract the audience and reduce the value and impact of what you are telling them. Reviewing recordings of presentations can be helpful to determine whether presenters are using filler phrases. Instead of manually reviewing […]
Amazon EC2 DL1 instances Deep Dive
This post is written by Amr Ragab, Principal Solutions Architect, Amazon EC2. AWS is excited to announce that the new Amazon Elastic Compute Cloud (Amazon EC2) DL1 instances are now generally available in US-East (N. Virginia) and US-West (Oregon). DL1 provides up to 40% better price performance for training deep learning models as compared to […]
Build workflows for Amazon Forecast with AWS Step Functions
This post shows how to create a Step Functions workflow for Forecast using AWS SDK service integrations, which allows you to use over 200 with AWS API actions. It shows two patterns for handling asynchronous tasks. The first pattern queries the describe-* API repeatedly and the second pattern uses the “Retry” option. This simplifies the development of workflows because in many cases they can replace Lambda functions.
Creating a serverless face blurring service for photos in Amazon S3
A serverless face blurring service can provide a simpler way to process photos in workloads with large amounts of traffic. This post introduces an example application that blurs faces when images are saved in an S3 bucket. The S3 PutObject event invokes a Lambda function that uses Amazon Rekognition to detect faces and GraphicsMagick to process the images.
Building a serverless multiplayer game that scales: Part 2
This post shows how you can add scaling support for a game via automation. The example uses Amazon Rekognition to check images for unacceptable content and uses asynchronous architecture patterns with Step Functions and HTTP WebPush.
Deploying machine learning models with serverless templates
This post written by Sean Wilkinson, Machine Learning Specialist Solutions Architect, and Newton Jain, Senior Product Manager for Lambda After designing and training machine learning models, data scientists deploy the models so applications can use them. AWS Lambda is a compute service that lets you run code without provisioning or managing servers. Lambda’s pay-per-request billing, automatic […]
Processing satellite imagery with serverless architecture
This post shows how to deploy an imagery processing pipeline in the AWS Cloud. It is decoupled to allow both pre and post-processing extensions to be integrated into the pipeline more easily. Visit the code repository for further information.
Building an image searching solution with the AWS CDK
This post discusses a fully serverless architecture for searching images based on their contents. It shows how this architecture is decoupled and stateless by using S3 events, SQS messages, an EventBridge bus, and Amazon Aurora Serverless.