Data lakehouse provider Databricks has announced four significant updates to its portfolio, aimed at empowering enterprises to have greater control over the development of their AI-based applications. The new features are designed to simplify AI development, deployment, and governance, ultimately increasing the stickiness of Databricks' offerings and bringing the company closer to its enterprise users.

One of the key updates is Centralized Governance, a feature currently in public preview, which enables enterprises to govern large language models, both open and closed source, within Mosaic AI Gateway. This capability is seen as a major simplifier by industry experts, ensuring consistent security, access controls, and compliance, while also reducing costs by eliminating duplicates and streamlining licensing fees.

Another significant update is Provision-Less Batch Inference, a novel way to run batch inference via Mosaic AI with a single SQL query. This capability, also in public preview, eliminates the need to set up infrastructure in advance, making it easier to scale AI and reducing costs by only using resources when needed. Industry experts believe this feature will make batch inference more accessible to data analysts without MLOps expertise, opening up new possibilities for AI deployment.

Databricks has also upgraded its previously released Agent Evaluation Review App, allowing domain experts to provide evaluations, send traces for labeling, and define custom evaluation criteria without needing spreadsheets or custom-built applications. This update is expected to drive systematic accuracy improvements in AI agent performance.

In addition, the company has introduced the AI/BI Genie Conversation API suite, currently in public preview, which enables developers to embed natural language-based chatbots directly into custom-built apps or productivity tools, such as Microsoft Teams, SharePoint, and Slack. The Genie API is a no-code tool that allows users to analyze data by asking questions in natural language, producing visualizations to explain the data.

Industry analysts view these updates as Databricks' strategy to increase its appeal to enterprise users and expand its presence in the AI market. By unifying the data-to-AI pipeline, Databricks is creating a platform that handles everything from raw data to operational AI, reducing the need for other products and increasing revenue by expanding the user base within enterprises.

In the agentic space, Databricks' technical approach is seen as an advantage over other vendors, allowing for the creation of more complex agents that can potentially automate activities in any domain with data. However, this advantage comes at the expense of who can create these agents, with less likelihood of business users being able to do so.

As the AI landscape continues to evolve, Databricks' updates are expected to have a significant impact on the industry, driving innovation and adoption in AI development and deployment.