Games earn more money than movies and music combined. Games also generate a lot of data.
How can that data best be used.
This session walks through how to develop a fully automated and scalable Machine Learning pipeline by the example from an innovative gaming company whose games are played by millions of people every day, meaning data growth within terabytes that can be used to produce great products and generate insights on improving the product.
Wildlife leverages data to drive product development lifecycle and deploys data science to drive core product decisions and features, which helps the company by keeping ahead of the market. We will also cover one of the use cases which is improving user acquisition through improved LTV models and the use of Apache Spark. Spark’s distributed computing enabled Data Scientists to run more models in parallel and they can innovate faster by onboarding more Machine Learning use cases. For example, using Spark allowed the company to have around 30 models for different kinds of tasks in production.