Friday, May 24, 2024

Qubrid AI Enhances Enterprise AI Development with New AI Model Studio Within AI Hub Deployed on GPU Cloud

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Qubrid AI, a leading provider of enterprise AI solutions, announced an update to its AI Hub platform with the launch of AI Model Studio. This innovative addition empowers businesses to rapidly develop, tune, and deploy cutting-edge AI models with ease and efficiency.

AI Model Studio running on latest GPU Cloud infrastructure serves as a centralized hub for all stages of model development. It offers businesses a user-friendly interface to interact with a library of pre-trained, industry-leading open-source models, spanning domains such as natural language processing, computer vision, and more. This allows businesses to quickly identify the ideal model for their specific needs without requiring extensive coding expertise.

“We understand that businesses want to harness the power of AI quickly and efficiently,” said Ujjwal Rajbhandari, CTO of Qubrid AI. “The AI Model Studio democratizes AI development, allowing enterprises to leverage pre-trained models as a springboard for innovation. They can then fine-tune these models with their own data, ensuring optimal performance for their unique use cases.”

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Beyond model selection, AI Model Studio simplifies the process of model tuning and optimization. Businesses can leverage intuitive tools and powerful compute resources within AI Hub to fine-tune pre-trained models with their proprietary data.

Deployment is equally streamlined, with AI Model Studio offering seamless integration with existing applications. Businesses can quickly deploy their optimized models with just a few clicks, significantly reducing time-to-market and accelerating the realization of tangible business value.

Key features of AI Model Studio:

  • Library of pre-trained open-source models: Explore and choose from a wide array of industry-leading models. Models include Gemma-2B, Yolo-V8, Falcon-7B, Stable Diffusion, and more.
  • Intuitive interface for model tuning and optimization: Fine-tune models using your own data for enhanced performance.
  • Powerful compute resources for efficient training: Leverage AI Hub’s compute platform to accelerate model development.
  • Seamless deployment and integration: Easily deploy models and integrate them into existing applications.

Source: PRNewsWire


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