RIOT GAMES RESOURCES

• Review the League of Legends Esports Data Technical Doc to learn more about the esports data provided for this hackathon, data access, thought starters, and important technical caveats and considerations.
  • Note: The Riot Team has been updating the doc. Please remember to check for the most updated version of the doc (Last Updated: Time - Date) is listed at the top!
• For participants submitting an API, please use the following methods and response model.

AWS RESOURCES

• Get started by signing up for a free AWS account.

AWS offers a number of services that can help you take on the challenge of developing a Global Power Rankings system for League of Legends Esports teams.

• Amazon CodeWhisperer is a free-to-use AI coding companion that can generate code suggestions ranging from snippets to full functions in real time based on your comments and existing code. CodeWhisperer can provide code suggestions for 15 programming languages including Python, Java, JavaScript, PHP, Ruby, SQL, and more. It also allows you to work within your favorite IDEs including VS Code, IntelliJ IDEA, AWS Cloud9, AWS Lambda console, JupyterLab and Amazon SageMaker Studio. And, CodeWhisperer is optimized for use with AWS services including Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, and Amazon Simple Storage Service (Amazon S3) (where the LoL Esports data for this hackathon is stored).

• For ML enthusiasts, consider using Amazon SageMaker. SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a few clicks from SageMaker Studio or the SageMaker console. You can find a variety of tutorials on SageMaker to help you get started, and several examples on GitHub.

• For data scientists and ML practitioners looking to build models using ML frameworks like TensorFlow, PyTorch, Apache MXNet, and others, you can utilize Amazon Elastic Compute Cloud (EC2) and AWS Deep Learning AMIs (what is the AWS Deep Learning AMI?).

• Those who are familiar with core cloud infrastructure but don’t have an ML background may opt to use relational/noSQL databases for data exploration, and Amazon Relational Database Service (RDS) or Amazon DynamoDB can meet your needs there.

• If you'd like to take a more statistical approach to the challenge, AWS offers a number of services that can help including Amazon Redshift, Amazon Athena, AWS Glue, Amazon OpenSearch Service, Amazon Elastic MapReduce (EMR), and Amazon QuickSight.

  • We've included scripts and documentation for getting started exploring the data with Amazon Athena and AWS Glue.

Most of the pages linked above have a Resources tab where you can view tutorials and examples you might find useful.

AWS will provide US $100 in AWS Promotional Credits to individuals registered for this hackathon, while supplies last. Check out the details here.

CONNECT WITH US

Looking for a team or have questions? Join us:

• Devpost Discord

• Discussion Forum