Databricks Introduces Scalable Batch Inference Model Serving [Video]

Databricks, the data and AI company, has announced a new feature aimed at enhancing the efficiency of large language model (LLM) inference with its Mosaic AI Model Serving. 

The company says this innovation allows for simple, fast, and scalable batch processing of LLMs, making it easier for organisations to deploy these models in production environments to analyse unstructured data.

The new model supports batch inference, allowing users to process multiple requests simultaneously, rather than one at a time. Databricks claims it enhances throughput and reduces latency, which is vital for real-time applications. Designed for ease of use, it provides a straightforward interface for users to quickly set up and manage LLM inference tasks without extensive coding.

Mosaic AI Model Serving efficiently scales with demand, enabling organisations to dynamically adjust resources based on workload for optimal performance during peak times. This feature integrates with the Databricks platform, using existing data lakes and collaborative notebooks …