Go-Jek, Indonesia’s first billion-dollar startup, has seen an incredible amount of growth in both users and data over the past two years. Many of the ride-hailing company's services are backed by machine learning models. Models range from driver allocation, to dynamic surge pricing, to food recommendation, and process millions of bookings every day, leading to substantial increases in revenue and customer retention.
Building a feature platform has allowed Go-Jek to rapidly iterate and launch machine learning models into production. The platform allows for the creation, storage, access, and discovery of features by both data scientists and models in production. It supports both low latency and high throughput access in serving, as well as high volume queries of historic feature data during training.
The platform has dramatically decreased the time to market for their ML systems, while simultaneously increasing predictive accuracy. Find out more about the challenges Go-Jek faced while building the feature platform, the lessons they learned, and how they ultimately delivered a system that would allow them to scale ML.
Willem leads the Data Science Platform Team at GO-JEK. His main focus areas are building data and ML platforms, allowing organizations to scale ML and drive decision making. The GO-JEK Data Science Platform supports a wide variety of models and handles over 100 million orders every month. Models include recommendation systems, driver allocation, forecasting, anomaly detection, routing, and more. In another life, Willem founded and sold a networking startup and worked as a software engineer in industrial control systems.
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