Introduction In this post, we outline how we transformed the way we serve data for our machine learning (ML) models and why we chose Amazon Aurora Postgres as the storage layer for our new feature store. At Grab, we have always been at the forefront of leveraging technology to enhance our services and provide the best possible experience for our platform users. This journey has led us to transition from a traditional approach to a more sophisticated and efficient ML feature store. Over the ye...| Grab Tech
When you're running 1000+ microservices across Southeast Asia's most complex transport and delivery platform, 'good enough' stops being good enough. Discover how Grab tackled the challenge of migrating from Consul to Istio across a hybrid infrastructure spanning AWS and GCP, separate AWS organizations, and diverse deployment models. This isn't your typical service mesh migration story. We share the real challenges of designing resilient architecture for massive scale, the unconventional decis...| Grab Tech
DispatchGym is a research framework that supports reinforcement learning (RL) studies for dispatch systems. A system that matches bookings with drivers. Designed to be efficient, cost-effective, and accessible, this article outlines its principles, research benefits, and real-world applications.| Grab Tech
Abstract The Integrity Data Platform (IDP) team decided to rewrite one of our heavy Queries Per Second (QPS) Golang microservices in Rust. It resulted in 70% infrastructure savings at a similar performance, but was not without its pitfalls. This article will elaborate on: How we picked what to rewrite in Rust. Approach taken to tackle the rewrite. The pitfalls and speed bumps along the way. Was it worthwhile? Introduction Grab is predominantly based on a microservice architecture, with the va...| Grab Tech
Introducing FlinkSQL interactive solution to enhance real-time stream processing exploration. The new system simplifies stream processing development, automates production workflows and democratises access to real-time insights. Read on about our journey that begun at addressing challenges encountered with the previous Zeppelin notebook-based solution to the current state of integration with and productionisation of FlinkSQL.| Grab Tech
Introduction Grab, Southeast Asia’s leading superapp, has created many internal applications to support its diverse range of internal and external business needs. Authentication1 and authorisation2 serve as fundamental components of application development, as robust identity and access management are essential for all systems. We recognised the need for a centralised internal system to manage access, authentication, and authorisation. This system would streamline access management, ensure ...| Grab Tech
Introduction In March 2023, I embarked on a mission to explore the potential of Large Language Models (LLMs) within Grab. What started off as an attempt to solve a specific problem—reducing the burden on our ML Platform team’s support channels, ended up becoming something much bigger. The creation of GrabGPT, an internal ChatGPT-like tool that has transformed how folks in Grab interact with AI. This is the story of how a failed experiment led to one of Grab’s most impactful internal too...| Grab Tech
