ClearML Integrates Vector Image Search and Vector Databases for AI Builders, Supercharging RAG Development

AI builders can now use native vector image search to gain deeper data insights, streamline exploration, and securely accelerate the development of RAG systems

ClearML, the leading open-source AI infrastructure platform, today announced that it has introduced native vector image search and an integrated vector database for RAG, empowering AI teams to explore, sample, and analyze their data with unprecedented ease. This new feature, integrated within ClearML’s Hyper-Datasets, enables AI builders to gain deeper data insights, streamline exploration, and securely accelerate the development of Retrieval-Augmented Generation (RAG) systems. Customers can effortlessly implement RAG in their GenAI applications and AI agents, streamlining workflows, improving data quality assessment, and accelerating the development of high-performance machine learning models and RAG-enabled LLMs.

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Prior to this new development, building RAG systems and implementing vector image search demanded a complex, fragmented stack of multiple tools, databases, and security layers, forcing AI teams to integrate multiple solutions manually. Combined with ClearML’s GenAI App Engine, which simplifies embedding model deployment, AI teams can frictionlessly develop RAG pipelines, eliminating inefficiencies and enabling a unified, end-to-end AI development experience. With ClearML, AI teams now have a secure, streamlined process for creating and launching a RAG system on a single platform using ClearML’s integrated vector database capabilities.

As well, ClearML’s automated logging and tracking extends into vector databases, which are logged and versioned. That gives AI builders an easy back-up so they can effortlessly revert back to a previous version if needed, which is critical for model reproducibility, data integrity, or if there are any performance issues.

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“AI builders creating GenAI applications need seamless, secure, and scalable solutions to explore and manage their data,” said Moses Guttmann, CEO and Co-founder of ClearML. “With our new vector document search and image search and integrated vector database support, we’re removing complexity and enabling teams to build RAG systems faster and with greater confidence. In the event of a data integrity or performance degradation issue, AI builders have the flexibility to roll back to previous versions of their databases. By bringing everything under one roof – data exploration, vector search, embedding model deployment, and security – ClearML continues to provide the most comprehensive AI infrastructure platform, helping organizations accelerate innovation while maintaining full control over their workflows.”

ClearML’s Hyper-Datasets optimize unstructured data management for rapid model development. With metadata-driven controls and seamless orchestration, this feature empowers teams to maximize performance without added complexity. The company’s GenAI App Engine provides the flexibility needed for developers to launch LLMs on top of its Infrastructure Control Plane that manages compute access, usage and performance monitoring, and security. Companies can use an off-the-shelf LLM with ClearML’s streamlined interface and integrated orchestration, or use their own fine-tuned model to jumpstart testing models for specific use cases and get GenAI apps into production faster.

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