Databricks Unveils Vector Search and Model Serving Updates for RAG Applications

Databricks announces general availability of Vector Search and updates to Model Serving, enhancing its Data Intelligence Platform for building high-quality Retrieval Augmented Generation applications. These updates aim to simplify the development and deployment of reliable generative AI experiences for enterprises.

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Nitish Verma
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Databricks Unveils Vector Search and Model Serving Updates for RAG Applications

Databricks Unveils Vector Search and Model Serving Updates for RAG Applications

Databricks, a leading provider of data intelligence and AI solutions, has announced the general availability of Vector Search and major updates to Model Serving. These updates aim to make it easy for enterprises to build high-quality Retrieval Augmented Generation (RAG) applications with native capabilities available directly in the Databricks Data Intelligence Platform.

The announcement comes at a time when businesses are eager to deploy generative AI applications but are held back by concerns over toxic content, leaks of sensitive data, and hallucinations. RAG applications have the potential to address these challenges by providing a more robust and reliable approach to generative AI.

Why this matters: As businesses increasingly rely on generative AI, the ability to build high-quality RAG applications will be crucial in mitigating the risks associated with traditional language models. Widespread adoption of RAG applications could lead to more accurate and trustworthy AI experiences, ultimately transforming the way businesses interact with customers and employees.

Databricks first introduced RAG in December 2023 as part of its efforts to support enterprises in building GenAI applications. The latest updates to Vector Search and Model Serving further enhance the platform's capabilities in this area.

Vector Search enables enterprises to efficiently search and retrieve relevant information from large datasets, which is a critical component of RAG applications. The general availability of Vector Search means that enterprises can now leverage this capability in production environments with full support from Databricks.

Model Serving, on the other hand, simplifies the deployment and management of machine learning models in production. The updates to Model Serving make it easier for enterprises to build and deploy RAG models at scale, with features like automatic scaling, monitoring, and versioning.

The combination of Vector Search and Model Serving updates provides enterprises with a comprehensive platform for building and deploying RAG applications. By leveraging these capabilities, businesses can create more accurate, contextually relevant, and safe generative AI experiences for their customers and employees.

Databricks' commitment to supporting enterprises in their GenAI journey is evident from these latest updates. As more businesses look to harness the power of generative AI, platforms like Databricks will play a crucial role in enabling them to build high-quality applications while addressing the challenges associated with traditional language models. The general availability of Vector Search and updates to Model Serving mark a significant step forward in making RAG applications more accessible and reliable for enterprises.