Category: Engineering

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 […]

All the code is available in this GitHub repository. Introduction Synchronizing data from external relational databases like Oracle, MySQL, or a data warehouse into the Databricks Data Intelligence Platform is a common use case. Databricks offers multiple approaches ranging from LakeFlow Connect’s simple and efficient ingestion connectors to Delta Live Tables’ (DLT) flexibility with APPLY […]

We’re excited to announce the general availability of hybrid search in Mosaic AI Vector Search. Hybrid search is a powerful feature that combines the strengths of pre-trained embedding models with the flexibility of keyword search. In this blog post, we’ll explain why hybrid search is important, how it works, and how you can use it […]

We are excited to announce that Graviton, the ARM-based CPU instance offered by AWS, is now supported on the Databricks ML Runtime cluster. There are several ways that Graviton instances provide value for machine learning workloads: Speedups for various machine learning libraries: ML libraries like XGBoost, LightGBM, Spark MLlib, and Databricks Feature Engineering could see up to 30-50% […]