Arthur Gola is the head of product data science at Wildlife Studios, where he leads data scientists to optimize the company’s mobile games through data-driven decision making and user experience personalization. Previously, he was a data science consultant for big corporations, having developed projects such as recommending the assortment of products for the physical stores of a large retailer. Arthur studied mechatronics engineering at the University of São Paulo, Brazil, and was once an athlete, achieving the title of national rowing champion five times.
November 18, 2020 04:00 PM PT
Games earn more money than movies and music combined. That means a lot of data is generated as well. One of the development considerations for ML Pipeline is that it must be easy to use, maintain, and integrate. However, it doesn't necessarily have to be developed from scratch. By using well-known libraries/frameworks and choice of efficient tools whenever possible, we can avoid "reinventing the wheel", making it flexible and extensible.
Moreover, a fully automated ML pipeline must be reproducible at any point in time for any model which allows for faster development and easy ways to debug/test each step of the model. 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.
Speakers: Vini Jaiswal and Arthur Gola