Databricks brings large language models (LLMs) to SQL and MLflow 2.3

Recently, Databricks is continuing to expand its efforts to democratize AI , announcing a pair of technology updates designed to help make it easier for enterprises to benefit from and use SQL to perform data analysis on large language models (LLMs).

The updates include the open-source MLflow 2.3 milestone that will make it easier for organizations to manage and deploy machine learning (ML) models, particularly transformer-based models hosted on Hugging Face. MLflow is a widely used technology effort led by Databricks that simplifies ML life cycle management, from experimentation to deployment, by providing tools for tracking, packaging and sharing models.

Databricks is also opening up LLMs to data analysts by enabling support for SQL (structured query language) queries. SQL is commonly used for querying databases and performing data analytics.

The recent updates are the latest in a series of AI efforts from Databricks within the last few weeks as the company looks to help make it easier for organizations to benefit from AI. Earlier, on March 24, Databricks announced the initial release of its open-source Dolly ChatGPT-type project, which was quickly followed up a few weeks later April 12 with Dolly 2.0. The new MLflow and SQL updates announced today will help further advance Dolly, as well as the usage of other LLMs, by making it easier for users to implement and run the technology to help enterprises gain real business benefits from their data.

Databricks isn’t just about AI. At its core, the company is about data, having coined the term data lakehouse and offering a cloud-based data lakehouse platform based on its open-source Delta Lake technology. According to Databricks cofounder and VP of engineering Patrick Wendell, organizations turn to his company to do “interesting things” with data.

“There’s two big categories of stuff people do with data: one is they ask questions about what happened in the past, so they’re doing some analytical processing,” Wendell told VentureBeat. “The other one is they’re building models to predict the future and, you know, we call that machine learning.”

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