Facial recognition technology is one of the many modern technologies that previously only appeared in science fiction movies. The roots of this technology can be traced back to the 1960s and have since grown dramatically due to the rise of deep learning techniques and accelerated digital transformation in recent years.
In this blog post, we will talk about the various applications of facial recognition technology in Grab, as well as provide details of the technical components that build up this technology.
In an introductory article, we talked about the importance of Graph Networks in fraud detection. In this article, we will be adding some further context on graphs, graph technology and some common use cases.
Connectivity is the most prominent feature of today’s networks and systems. From molecular interactions, social networks and communication systems to power grids, shopping experiences or even supply chains, networks relating to real-world systems are not random. This means that these connections are not static and can be displayed differently at different times. Simple statistical analysis is insufficient to effectively characterise, let alone forecast, networked system behaviour.
As the world becomes more interconnected and systems become more complex, it is more important to employ technologies that are built to take advantage of relationships and their dynamic properties. There is no doubt that graphs have sparked a lot of attention because they are seen as a means to get insights from related data. Graph theory-based approaches show the concepts underlying the behaviour of massively complex systems and networks.
Trustworthy experiments are key to making sound decisions, so analysts and data scientists put a lot of effort into analysing them and making business impacts. An extension of Grab’s Experimentation (GrabX) platform, Automated Experiment Analysis is one of Grab’s data products that helps automate statistical analyses of experiments. It also provides automatic experimental data pipelines and customised tests for different types of experiments.
Prior to 2021, Grab’s search architecture was designed to only support textual matching, which takes in a user query and looks for exact matches within the ecosystem through an inverted index. This legacy system meant that only textual matching results could be fetched.
In the second half of 2021, the Deliveries search team worked on improving this architecture to make it smarter, more scalable and also unlock future growth for different search use cases at Grab.
Read to find out how Grab is using the Docs-as-Code approach to improve technical documentation.
This article illustrates how the Cauldron Machine Learning (ML) Platform team uses GitLab parent-child pipelines to dynamically generate GitLab CI files to solve several limitations of GitLab for large repositories.
Grab’s real-time data platform team (Coban) covers the importance of moving data in and out of Kafka easily and how Kafka Connect helps with that.
Running batch jobs targeting a large user base is a challenge. Find out how we designed our system to tackle the challenge at scale.
Telematics is a collection of sensor data such as accelerometer data, gyroscope data, and GPS data that a driver’s mobile phone provides, and we collect, during the ride. With this information, we apply data science logic to detect traffic events such as harsh braking, acceleration, cornering, and unsafe lane changes, in order to help improve our consumers’ ride experience.
Typically, modern applications use various database engines for their service needs; within Grab, these would be MySQL, Aurora and DynamoDB. Lately, the Caspian team has observed an increasing need to consume real-time data for many service teams. These real-time changes in database records help to support online and offline business decisions for hundreds of teams.
Because of that, we have invested time into synchronising data from MySQL, Aurora and Dynamodb to the message queue, i.e. Kafka. In this blog, we share how real-time data ingestion has helped since it was launched.
Learn about the challenges of points rewarding and how GrabRewards Points are rewarded for different Grab offerings.
In large organisations, it is a common practice to isolate the cloud resources of different verticals. Amazon Web Services (AWS) Virtual Private Cloud (VPC) is a convenient way of doing so. At Grab, while our core AWS services reside in a main VPC, a number of Grab Tech Families (TFs) have their own dedicated VPC. One such example is GrabKios. Previously known as “Kudo”, GrabKios was acquired by Grab in 2017 and has always been residing in its own AWS account and dedicated VPC.
In this article, we explore how we exposed an Apache Kafka cluster across multiple Availability Zones (AZs) in Grab’s main VPC, to producers and consumers residing in the GrabKios VPC, via a VPC Endpoint Service. This design is part of Coban unified stream processing platform at Grab.
GrabAds is a service that provides businesses with an opportunity to market their products to Grab’s consumer base. During the pandemic, as the demand for food delivery grew, we realised that ads could be a service we offer to our small restaurant merchant-partners to expand their reach. This would allow them to not only mitigate the loss of in-person traffic but also grow by attracting more customers.
Many of these small merchant-partners had no experience with digital advertising and we provided an easy-to-use, scalable option that could match their business size. On the other side of the equation, our large network of merchant-partners provided consumers with more choices. For hungry consumers stuck at home, personalised ads and promotions helped them satisfy their cravings, thus fulfilling their intent of opening the Grab app in the first place!
In recent years, Identity and Access Management has gained importance within technology industries as attackers continue to target large corporations in order to gain access to private data and services. To address this issue, the Grab Identity team has been using a 6-digit PIN to authenticate a user during a sensitive transaction such as accessing a GrabPay Wallet. We also use SMS one-time passwords (OTPs) to log a user into the application.
We look at existing mechanisms that Grab uses to authenticate its users and how biometric authentication helps strengthen application security and save costs. We also look at the various technical decisions taken to ensure the robustness of this feature as well as some key learnings.
In 2016, Clayton Christensen, a Harvard Business School professor, wrote a book called Competing Against Luck. In his book, he talked about the kind of jobs that exist in our everyday life and how we can uncover hidden jobs through the act of non-consumption. Non-consumption is the inability for a consumer to fulfil an important Job to be Done (JTBD).
JTBD is a framework; it is a different way of looking at consumer goals and is based on the notion that people buy products and services to get a job done. In this article, we will walk through what the JTBD framework is, look at an example of a popular JTBD, and look at how we use the JTBD framework in one of Grab’s services.
Learn about the different optimisation techniques when building a search index.