Unlock innovation and creativity
With 10X better-performing algorithms and 2X the performance compared to other cloud providers, AWS Machine Learning is focused on solving some of the toughest challenges facing game developers today.
Build, train, and deploy ML models fast so you spend more time making predictions on player behavior and taking action to engage and delight players.
Augment your game with artificial intelligence that help you build better, more immersive experiences for your players in a shorter period of time.
Featured use cases for AWS Machine Learning
Click each use case to learn more.
Automated image analysis
Save hours of manual data labeling by using machine learning to automatically tag images in your asset library.
How automated image analysis works
If an artist takes 40 seconds to open a file, look at the image, form an opinion, and write multiple tags to it, then store it in a database, it would take them over 55 working hours to process 5,000 files.
Let artists focus on designing an immersive world for players. Using Amazon Rekognition, AWS’s image recognition that utilizes machine learning, asset labeling becomes simpler and exponentially faster. In our test code, uploading at a speed of 200 megabits per second, it took a little less than 3 minutes to complete image labeling.
Services used to automate image analysis
Virtual voice actors
Use the power of the cloud to add lifelike voice into your games without having to hire voice actors, or pre-build in-game dialog before hiring the voice actor to produce the final voice track.
How virtual voice actors work
Leveraging the deep learning technologies of Amazon Polly gives you a quick and frictionless way to generate lifelike speech in your games, with support for 24 different languages and 50 unique voices.
What is Amazon Polly?
Amazon Polly is a Text-to-Speech (TTS) service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice.
What is Amazon Lumberyard?
Amazon Lumberyard is a free, cross-platform AAA game engine deeply integrated with AWS and Twitch – with full source code provided.
Detect player fraud
Catch fraudulent activity for in-game purchases and more before it hits your bottomline.
How fraud detection works
Fraud detection using Machine Learning enables you to execute automated transaction processing on a datasest, detecting potentially fraudulent activity and flagging that activity for review. The diagram below presents the architecture you can automatically deploy using an implementation guide and accompanying AWS CloudFormation template.
Services to use for fraud detection
Leverage the Fraud Detection Using Machine Learning solution out of-the-box, or as a reference implementation for detecting player fraud detection on credit card transactions for in-app purchases, premium subscriptions, or in-game marketplaces.