Ahmed Bilal

Nov 14, 2024 | databricks.com | Jeffrey Chen |Ahmed Bilal |Margaret Qian |Bay Foley-Cox

Many AI use cases now depend on transforming unstructured inputs into structured data. Developers are increasingly relying on LLMs to extract structured data from raw documents, build assistants that retrieve data from API sources, and create agents capable of taking action. Each of these use cases requires the model to generate outputs that adhere to a structured format.

Oct 30, 2024 | databricks.com | Ahmed Bilal |Youngbin Kim |Ankit Mathur |Kasey Uhlenhuth

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 in a different region can significantly increase both the request and response times.

Oct 22, 2024 | databricks.com | Ahmed Bilal |Youngbin Kim |Ankit Mathur |Daniel King

Over the years, organizations have amassed a vast amount of unstructured text data—documents, reports, and emails—but extracting meaningful insights has remained a challenge. Large Language Models (LLMs) now offer a scalable way to analyze this data, with batch inference as the most efficient solution. However, many tools still focus on online inference, leaving a gap for better batch processing capabilities.

Oct 1, 2024 | databricks.com | Ahmed Bilal |Kasey Uhlenhuth |Siddharth Murching |Akhil Gupta

Many of our customers are shifting from monolithic prompts with general-purpose models to specialized compound AI systems to achieve the quality needed for production-ready GenAI apps. In July, welaunched the Agent Framework and Agent Evaluation, now used by many enterprises to build agentic apps likeRetrieval Augmented Generation (RAG.

Sep 25, 2024 | databricks.com | Daniel King |Hanlin Tang |Patrick Wendell |Ahmed Bilal

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 provide key components for customers on Databricks to build compound AI systems that enable data intelligence – connecting these models to their enterprise data.