Category: Engineering

Imagine a world where finding the right data is like searching for a needle in a haystack. In today’s data-driven landscape, companies are drowning in a sea of information, struggling to navigate through countless datasets to uncover valuable insights. At Grab, we faced a similar challenge. With over 200,000 tables in our data lake, along […]

We are excited to partner with Meta to launch the latest models in the Llama 3 series on the Databricks Data Intelligence Platform. The small textual models in this Llama 3.2 release enable customers to build fast real-time systems, and the larger multi-modal models mark the first time the Llama models gain visual understanding. Both […]

In May 2023, Grab unveiled the Live Activity feature for iOS, which received positive feedback from users. Live Activity is a feature that enhances user experience by displaying a user interface (UI) outside of the app, delivering real-time updates and interactive content. At Grab, we leverage this feature to keep users informed about their order […]

We are excited to announce that Databricks now supports Amazon EC2 G6 instances powered by NVIDIA L4 Tensor Core GPUs. This addition marks a step forward in enabling more efficient and scalable data processing, machine learning, and AI workloads on the Databricks Data Intelligence Platform. Why AWS G6 GPU Instances? Amazon Web Services (AWS) G6 […]

Personal Access Tokens (PATs) are a convenient way to access services like Azure Databricks or Azure DevOps without logging in with your password. Today, many customers use Azure DevOps PAT tokens as Git credentials for remote repositories in Databricks Git folders (formerly Repos). Unfortunately, the use of PAT tokens comes with some downsides. In Azure DevOps, PAT tokens […]

In a previous blog, we introduced Trident, Grab’s internal marketing campaign platform. Trident empowers our marketing team to configure If This, Then That (IFTTT) logic and processes real-time events based on that. While we mainly covered how we scaled up the system to handle large volumes of real-time events, we did not explain the implementation […]

An improved answer-correctness judge in Agent Evaluation Agent Evaluation enables Databricks customers to define, measure, and understand how to improve the quality of agentic GenAI applications. Measuring the quality of ML outputs takes a new dimension of complexity for GenAI applications, especially in industry-specific contexts dealing with customer data: the inputs may comprise complex open-ended […]

Maintaining heavy equipment assets, such as oil rigs, agricultural combines, or fleets of vehicles, poses an extremely complex challenge for global companies. These assets are often spread across the globe, while their maintenance schedules and lifecycles are typically determined at a company-wide level. The failure of a key component can result in millions of dollars […]

Recommender systems (RecSys) have become an integral part of modern digital experiences, powering personalized content suggestions across various platforms. These sophisticated systems and algorithms analyze user behavior, preferences, and item characteristics to predict and recommend items of interest. In the era of big data and machine learning, recommender systems have evolved from simple collaborative filtering […]

Key to innovation and improvement in machine learning (ML) models is the ability for rapid iteration. Our team, Chimera, part of the Artificial Intelligence (AI) Platform team, provides the essential compute infrastructure, ML pipeline components, and backend services. This support enables our ML engineers, data scientists, and data analysts to efficiently experiment and develop ML […]