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Hugging Face

Künstliche Intelligenz

AI platform for NLP model building, training, and deployment.

Empowers enterprises with open-source tools and secure deployment options, ensuring robust data protection and model integrity.

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Kategorie
Datenschutz & Sicherheit
Datenschutz
Bedingungen

Hugging Face is a leading AI provider that enables users to build, train, and deploy natural language processing (NLP) models. The platform is known for its open-source library and community-oriented approach, allowing data scientists and engineers to share and build upon each other’s work.

Hugging Face offers a range of products and services, including Autotrain, Inference API, and on-premise and private cloud hosting options, catering primarily to large enterprises.

Security:

Security is a top priority at Hugging Face, with features such as SOC2 Type 2 certification, private repositories, access tokens, two-factor authentication, and malware scanning to ensure the security and integrity of user data and models.

  • Add a new serverless configuration in Promptitude: In Promptitude, go to Settings > Serverless and click the New Inference button at the bottom right. Available for Enterprise Plans.
  • In the slide-over, choose your cloud provider and name the connection (just use the provider name for starters).
  • Click Save, and you're ready to configure your provider and deploy your models.

➔ Follow step by step and find out the requirements for each cloud provider.

Users can leverage Hugging Face by accessing its open-source library, uploading or selecting pre-trained models, fine-tuning them for specific tasks, and deploying them using the Inference API or hosting options.

Hugging Face: Integrate your AI models seamlessly.

Use cases

  • Text Classification: Using models like BERT for sentiment analysis and sentence classification.
  • Language Translation: Utilizing models such as T5 for multilingual applications.
  • Content Creation: Leveraging GPT for generating human-like text in blog posts, articles, and marketing content.
  • Multimodal Applications: Employing CLIP for image classification and understanding visual content.
  • Multilingual Tasks: Using BLOOM for creating content and retrieving information across different languages.
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