How Does League Of Legends Deploy Machine Learning Models Into The Game?

Looking at how LOL deploys machine learning models with Ian Schweer - ML/MLOps Engineer @ Riot Games

I talk a lot about data infrastructure in this newsletter but I don’t often cover what people are actually doing with their data. So I was excited when I got the opportunity to work with Ian Schweer from Riot Games to discuss how their team deploys their machine learning models to League Of Legends.

Now let’s dig into how machine learning engineers deploy their models to League of Legends.

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League of Legends and TFT(Teamfight Tactics) are worldwide games that serve millions of players. League of Legends is a 5v5 MOBA (multiplayer online battle arena) game where 10 players log into a game, each picking a champion for their particular role in the game (Top lane, mid lane, bot lane, support, and jungle) and attempting to take the enemy base through fights with the opponent teams, competing over neutral objectives, and leveling up their characters.

Both TFT and League of Legends leverage the League game engine which our team supports.

The data team is responsible for:

1. Collecting all the in and out of game telemetry to understand our players

2. Understanding the league player base, and what they like and dislike. Most importantly, data scientists work with game designers to help understand what the game is doing right for our players, and wrong.

3. Helping game developers build and ship data-driven features

In this post, I want to share with you some challenges in telemetry, player behavior, and shipping data in game.