Category: Engineering

[ad_1] Introduction Coban – Grab’s real-time data streaming platform – has been operating Kafka on Kubernetes with Strimzi in production for about two years. In a previous article (Zero trust with Kafka), we explained how we leveraged Strimzi to enhance the security of our data streaming offering. In this article, we are going to describe […]

[ad_1] Today, Databricks is excited to announce support for Mixtral 8x7B in Model Serving. Mixtral 8x7B is a sparse Mixture of Experts (MoE) open language model that outperforms or matches many state-of-the-art models. It has the ability to handle long context lengths of up to 32k tokens (approximately 50 pages of text), and its MoE […]

[ad_1] For eBay sellers, social media can be an invaluable tool for publicizing, promoting, and popularizing their listings — but figuring out the best phrasing can feel challenging. A new eBay feature, powered by generative AI, makes the process of drafting a post as easy as clicking a button. We’re excited to announce AI-generated social […]

[ad_1] Launched in 2015, Grab’s Security bug bounty programme has achieved remarkable success and forged strong partnerships within a thriving bounty community. By holding quarterly campaigns with HackerOne, Grab has been dedicated to security and giving back to the global security community to research further. Over the years, Grab has paid over $700,000 in cumulative […]

[ad_1] Background In an era where Retrieval-Augmented Generation (RAG) is revolutionizing the way we interact with AI-driven applications, ensuring the efficiency and effectiveness of these systems has never been more essential. Databricks and MLflow are at the forefront of this innovation, offering streamlined solutions for the critical evaluation of GenAI applications.  This blog post guides […]

[ad_1] Like many other companies, Grab uses marketing communications to notify users of promotions or other news. If a user receives these notifications from multiple companies, it would be a form of information overload and they might even start considering these communications as spam. Over time, this could lead to some users revoking their consent to […]

[ad_1] Introduction Databricks Lakehouse Monitoring allows you to monitor all your data pipelines – from data to features to ML models – without additional tools and complexity. Built into Unity Catalog, you can track quality alongside governance and get deep insight into the performance of your data and AI assets. Lakehouse Monitoring is fully serverless […]

[ad_1] Following the announcements we made last week about Retrieval Augmented Generation (RAG), we’re excited to announce major updates to Model Serving. Databricks Model Serving now offers a unified interface, making it easier to experiment, customize, and productionize foundation models across all clouds and providers. This means you can create high-quality GenAI apps using the […]

[ad_1] Retrieval Augmented Generation (RAG) is an efficient mechanism to provide relevant data as context in Gen AI applications. Most RAG applications typically use vector indexes to search for relevant context from unstructured data such as documentation, wikis, and support tickets. Yesterday, we announced Databricks Vector Search Public Preview that helps with exactly that. However, […]

[ad_1] Following the announcement we made yesterday around Retrieval Augmented Generation (RAG), today, we’re excited to announce the public preview of Databricks Vector Search. We announced the private preview to a limited set of customers at the Data + AI Summit in June, which is now available to all our customers. Databricks Vector Search enables […]