AI and ML for Games

From detecting fraud and predicting player behavior, to automating speech recognition and playtesting, Artificial Intelligence (AI) and Machine Learning (ML) makes games smarter and development faster.

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Build intelligent game experiences

Build faster

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.

Operate your game smarter

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.

Create new experiences

Augment your game with artificial intelligence that help you build better, more immersive experiences for your players in a shorter period of time.

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“Using Amazon SageMaker and Amazon Personalize, we have automated, tailored recommendations starting on every user’s first day within the apps, resulting in a 15 percent increase in retention amongst these users. By reducing our dependency on our homegrown personalization tool, we have reduced our development time by 53 percent, enabling our teams to focus on the next set of opportunities to further improve experiences for our customers.”
—Robin Mizreh, Technical Lead, Voodoo
Click each use case to learn more.
  • Shapes plotted across a graph to represent metric monitoring

    Real-time sentiment monitoring

    Monitor the sentiment of a Twitch chat in real time with Amazon Comprehend.

    Understanding the sentiment of your player base is key in responding to feedback and building a game players want to play. Let’s say you’re getting ready to launch a risky new feature that you plan to test in a subset of channels. You have a good metric to optimize with an A/B test, but it’s a lagging indicator and you need to be able to roll back quickly if players get frustrated. 

    To reduce the risk, you build an application that monitors the sentiment of Twitch chats in real time. Then, you write a chat client that measures the sentiment of every message with Amazon Comprehend. The average sentiment by minute is displayed in an Amazon CloudWatch dashboard, so you can rest easy when your new feature goes live.

    The concepts covered in this workshop can be applied to other data source like text chat, discussion boards, and other social media channels.

    Take the workshop »

Not sure where to start?

Get started using Machine Learning and Artificial Intelligence with these helpful steps.



Detect fraud in games using machine learning

Learn how to get fraud detection using ML up and running, so you can train and run your own machine learning models that help detect in-game fraud.

Read the blog »



Automate game leveling with reinforcement learning

Learn how to automate game leveling using reinforcement learning with Amazon SageMaker.

Take the workshop »



Real-time sentiment monitoring

Monitor the sentiment of a Twitch chat in real time with Amazon Comprehend.

Take the workshop »



Identifying suspicious activity with an ML powered abuse detection pipeline

Learn how to leverage AWS machine learning and artificial intelligence to build a dynamic and performant end-to-end abuse detection pipeline.

Watch the presentation »

Featured Customer Story

Gearbox Software and MMOS use machine learning to create minigame that helps advance scientific research.

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Meet studios that innovate for their players with AWS

Promotional image for Angry Birds Dreamblast

Rovio reduces data collection time from years to hours using ML to automate level balance testing.

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Still image showing the tail of a Audi race car driving around a track in a video game

Sony Interactive Entertainment is modernizing its existing infrastructure to remotely render visual images and effects using AWS.

Watch the video »

Activision logo

Activision uses analytics and ML to personalize player engagement with millions of Call of Duty players each day.

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See related technical guides, webinars, white papers and much more.

Detecting fraud in games using machine learning

Learn the unique challenges of detecting in-game fraud and how to build a fraud detection pipeline using our implementation guide and AWS CloudFormation template.

Read the blog »
Understanding game changes and player behavior with graph databases

Discover how to answer difficult questions about changes to your game and player behavior using knowledge graphs and Amazon Neptune.

Watch now »
Using machine learning to understand a user community

In this guest post, learn how Oterlu AI set out to make people feel welcome and secure in online communities.

Read the blog »

New to AWS Game Tech?

Choose from a range of game developer courses, explore our ramp-up guide, or build your skills with a hands-on lab. 

Ready to start using AI and ML solutions from AWS?

Whether you’re a team of one or one thousand, we’d love to learn more about your game development needs.