SpicyGen
What is Xorbits?
Xorbits offers a comprehensive solution designed for enterprises seeking to leverage proprietary large-scale AI models. The platform streamlines the entire process, starting from data loading and pre-processing through to model fine-tuning and deployment, all manageable within the user's own infrastructure. This approach ensures data privacy and security, as sensitive data and models remain behind the enterprise's firewalls.
The platform emphasizes creating models tailored specifically to business requirements by allowing curation of content and data during the fine-tuning stage. Xorbits aims to make the deployment of large language models (LLMs) more cost-efficient and accelerate the time-to-production. It provides a 'batteries included' experience, simplifying the training and serving of large AI models at scale, requiring users primarily to point to their data.
The platform emphasizes creating models tailored specifically to business requirements by allowing curation of content and data during the fine-tuning stage. Xorbits aims to make the deployment of large language models (LLMs) more cost-efficient and accelerate the time-to-production. It provides a 'batteries included' experience, simplifying the training and serving of large AI models at scale, requiring users primarily to point to their data.
Features
- Data Processing: Facilitates data loading and pre-processing for AI model training.
- Model Fine-tuning: Allows customization of AI models tailored to specific data and business needs.
- Self-Hosted Deployment: Enables deployment of models within the user's own infrastructure, ensuring data privacy.
- Cost Efficiency: Aims for significant cost savings in deploying Large Language Models (LLMs).
- Scalable Training & Serving: Supports training and serving large AI models at scale out-of-the-box.
Use Cases
- Developing proprietary enterprise AI models.
- Fine-tuning large language models on custom datasets.
- Deploying generative AI applications within secure corporate environments.
- Streamlining the data-to-model pipeline for AI projects.
- Reducing costs associated with large AI model deployment.
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