Vincent was playing a card game against his girlfriend and he kept loosing. So he wanted to train a bot to play on his behalf. This is our story. We’re using AWS Lambda to get better at a card game named SushiGO. We make a small genetic algorithm in Python that uses AWS Lambda as a backend. The talk consists of these parts: Quick Explanation of the rules of the SushiGo Card Game Translation of real life to an algorithm Explain why this problem needs a lot of CPU Explain why AWS Lambda fits the simulation use-case How to quickly hack Concurrency in Python How to deploy lambda very quickly with chalice Experimentation Results This talk will discuss an algorithm that we’ve tried to improve in three ways: Applying simple maths to make the search algorithm better Throwing lots (lots!) of CPU’s against the problem by leveraging AWS Lambda and python concurrency We will conclude by discussing whether or not AWS Lambda is suitable for a gridsearch/grid simulation (hint, it’s not meant for this task, but it actually kind of works very well).