Key benefits of going serverless
Key benefits of going serverless
Serverless model training
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Effortlessly train models with support for industry-leading AI frameworks in fully managed, auto-scaling environments that handle distributed training.
Serverless fine-tuning
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Fine-tune LLMs or multi-modal models for text, vision, audio, & embeddings with out-of-the-box fine-tuning support. Leverage advanced hyperparameter tuning, LoRa settings, seamless dataset integration & built-in observability integrations.
Serverless high-performance inference
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Deploy high-performing inference pipelines with low latency and automatic scaling, ensuring seamless integration into your downstream applications.
Refine models with human feedback
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Improve model accuracy and alignment by incorporating human preferences through integrated Reinforcement Learning with Human Feedback capabilities.
Explore real-world serverless AI use cases
1
Efficient model training
Scale AI model training without managing infrastructure. Utilise fully managed, auto-scaling environments that handle distributed compute automatically.
2
Effortless fine-tuning
Customise existing models for specific tasks. Easily fine-tune text, vision, audio, and embedding models using integrated tools like Hugging Face, advanced tuning options, and seamless dataset handling in a managed, serverless environment.
3
Serve AI predictions serverless
Make model predictions available in real-time. Deploy models as a production-grade, low-latency service with automatic scaling, intelligent routing, built-in security, and monitoring.
4
Refine model behaviour
Incorporate human preferences directly into the training loop using integrated Reinforcement Learning with Human Feedback capabilities to enhance model accuracy and alignment for better results.
Count on us for proven results
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