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What is Cerebrium?
Cerebrium is a platform that allows users to quickly and easily build, deploy, and monitor machine learning models with just a few lines of code. It provides a machine learning framework that simplifies the process of training, deploying, and monitoring models, without the need for extensive coding knowledge.
With Cerebrium, users can deploy serverless GPU models with major ML frameworks such as PyTorch, ONNX, and XGBoost in just one line of code. The tool also supports the deployment of prebuilt models that are optimized to run at sub-second latency, making it perfect for real-time applications.
In addition to model deployment, Cerebrium offers support for custom model deployments, allowing users to chain together multiple custom models to create unique functionality. It also provides automatic versioning and rollback options, making it easy to manage different versions of deployed models.
Cerebrium makes training effortless with its fine-tuning feature, which allows users to fine-tune smaller models for specific tasks, reducing costs and latency while increasing performance. The tool also supports the use of open-source models like GPT-Neo and Stable Diffusion, offering alternatives to proprietary models like GPT-3.
Monitoring models is made simple with Cerebrium, as it integrates with top ML observability platforms such as Arize and Censius. This allows users to receive alerts for prediction drift and compare different model versions, helping resolve issues quickly. Cerebrium is used by the teams at Twilio, Ramp and Writesonic
Cerebrium possible use cases:
Deploying serverless GPU models.
Creating custom model deployments.
Fine-tuning smaller models.
Monitoring ML models for prediction drift.
Comparing different model versions and their performance.
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