Features
- One-Click Deployments: Deploy models from major ML frameworks like Pytorch, Onnx, or HuggingFace with a single line of code. For users without their own models, we provide prebuilt models optimized for sub-second latency.
- Support for Major Frameworks: Cerebrium accommodates custom model deployments for all major frameworks, ensuring flexibility with Pytorch, Tensorflow, XGBoost, and more.
- Effortless Fine-Tuning: Fine-tune your models easily to reduce costs, latency, and enhance performance. With just a few lines of code, let us handle the infrastructure for you.
- Simple ML Monitoring: Integrate seamlessly with leading ML observability platforms for alerts on feature/prediction drift, model comparison, and swift issue resolution.
- Serverless GPU’s: Leverage our combination of Nvidia T4’s, A10’s, and A100’s, with charges only for your inference time.
Use Cases:
- Fast Model Deployment: Ideal for data scientists and engineers seeking quick and cost-effective deployment of ML models with Pytorch, Onnx, HuggingFace, and more.
- ML Model Fine-tuning: Perfect for users looking to fine-tune smaller models for specific tasks, improving performance, and reducing costs.
- ML Model Observability: Tailored for teams wanting to monitor feature and prediction drift, compare model versions, and respond promptly to issues.
Cerebrium’s ML platform, trusted and backed by users worldwide, simplifies the entire ML model management process from training and deployment to monitoring. Get started today with a free $10 credit!
