Product spotlight: Native LLM & multimodal support for hybrid evaluation

Labelbox is enhancing its data-centric AI platform to better support the growing demand for Generative AI and Large Language Models by introducing new features and partnerships that tackle the challenges of data labeling, model evaluation, and deployment. The platform now supports multimodal data labeling, AI-assisted data generation, and human-centric benchmarking, facilitating the transition to multimodal AI while emphasizing the importance of human evaluation. Recent updates include the LLM Human Preference Editor for optimized model configurations, video annotation improvements, and enhanced issue categorization, search, and sampling capabilities, which collectively streamline the labeling workflow. Furthermore, Labelbox has introduced production inferencing via HTTP REST API, model fine-tuning for YoloV8, and expanded video support in its Foundry platform, enhancing the model building and experimentation process. To address the challenges AI leaders face in scaling AI throughput and reducing development costs, the platform now features no-code warehouse integrations, improved SDK functionality, and optimized data management and export processes. Labelbox's partnerships with Google Cloud and LangSmith, along with its Labelbox Academy and various guides, provide comprehensive resources for AI development, enabling users to build high-performing AI applications. Through webinars, industry events, and a vibrant user community, Labelbox fosters knowledge sharing and collaboration, ensuring users can deploy cutting-edge AI applications effectively.

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