Category: Engineering

When serving machine learning models, the latency between requesting a prediction and receiving a response is one of the most critical metrics for the end user. Latency includes the time a request takes to reach the endpoint, be processed by the model, and then return to the user. Serving models to users that are based […]

Introduction GrabX is Grab’s central platform for product configuration management. It has the capacity to control any component within Grab’s backend systems through configurations that are hosted directly on GrabX. GrabX clients read these configurations through an SDK, which reads the configurations in a way that’s asynchronous and eventually consistent. As a result, it takes […]

We’re excited to announce the General Availability of Publish to Microsoft Power BI Service from Unity Catalog, an integration that makes it easy to create Power BI web reports from your Unity Catalog data in just a few clicks. This feature enables seamless catalog integration and data model sync, allowing you to publish datasets directly […]

We are updating this blog to show developers how to leverage the latest features of Databricks and the advancements in Spark. Most data warehouse developers are very familiar with the ever-present star schema. Introduced by Ralph Kimball in the 1990s, a star schema is used to denormalize business data into dimensions (like time and product) […]

Founded in 2017, Logically is a leader in using AI to augment clients’ intelligence capability. By processing and analyzing vast amounts of data from websites, social platforms, and other digital sources, Logically identifies potential risks, emerging threats, and critical narratives, organizing them into actionable insights that cybersecurity teams, product managers, and engagement leaders can act […]

Introduction As the complexity of data retrieval requirements continue to grow, traditional search methods often struggle to provide relevant and accurate results, especially for nuanced or conceptual queries. Vector similarity search has emerged as a powerful technique for finding semantically similar information. It refers to finding vectors in a large dataset that are most similar […]

Introduction Retrieval-Augmented Generation (RAG) is a powerful process that is designed to integrate direct function calling to answer queries more efficiently by retrieving relevant information from a broad database. In the rapidly evolving business landscape, Data Analysts (DAs) are struggling with the growing number of data queries from stakeholders. The conventional method of manually writing […]

Introduction Retrieval-augmented generation (RAG) has revolutionized how enterprises harness their unstructured knowledge base using Large Language Models (LLMs), and its potential has far-reaching impacts. Intercontinental Exchange (ICE) is a global financial organization operating exchanges, clearing houses, data services, and mortgage technology, including the largest stock exchange group in the world, the New York Stock Exchange (NYSE). […]

Introduction Applying Large Language Models (LLMs) for code generation is becoming increasingly prevalent, as it helps you code faster and smarter. A primary concern with LLM-generated code is its correctness. Most open-source coding benchmarks are designed to evaluate general coding skills. But, in enterprise environments, the LLMs must be capable not only of general programming […]

The buzz around compound AI systems is real, and for good reason. Compound AI systems combine the best parts of multiple AI models, tools, and systems to solve complex problems that a single AI, no matter how powerful, might struggle to tackle efficiently. A Look Back: From Monolithic to Microservices Before diving into the magic […]