ML Model Deployment
Freemium
Cerebrium is a comprehensive platform designed to simplify the process of building, deploying, and monitoring machine learning models. With Cerebrium, users can quickly and easily deploy serverless GPU models using major ML frameworks such as PyTorch, ONNX, and XGBoost with just a single line of code. This makes it an ideal solution for those who may not have extensive coding knowledge but still need to implement robust machine learning solutions. The platform supports the deployment of prebuilt models optimized for sub-second latency, making it perfect for real-time applications.
Additionally, Cerebrium allows for custom model deployments, enabling users to chain together multiple custom models to create unique functionalities tailored to their specific needs. One of the standout features of Cerebrium is its automatic versioning and rollback options, which make it easy to manage different versions of deployed models. This ensures that users can quickly revert to previous versions if needed, minimizing downtime and potential issues. The platform also offers a fine-tuning feature, allowing users to fine-tune smaller models for specific tasks. This not only reduces costs and latency but also enhances performance. Cerebrium supports the use of open-source models like GPT-Neo and Stable Diffusion, providing alternatives to proprietary models such as GPT-3.
Monitoring is made simple with integrations to top ML observability platforms like Arize and Censius, enabling users to receive alerts for prediction drift and compare different model versions to resolve issues quickly. Trusted by teams at Twilio, Ramp, and Writesonic, Cerebrium is a reliable and efficient tool for machine learning engineers, data scientists, and AI developers.
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Deploy serverless GPU models
Real-time application support
Custom model deployments
Automatic versioning and rollback
Fine-tuning smaller models
Integrations with ML observability platforms
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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Effortless ML model deployment and inference
Simplify deep learning with intuitive AI modeling.
End-to-end monitoring and explanation for ML models
Create machine learning environments effortlessly
Easily build, train, and serve state-of-the-art models.
Compare multiple AI models in seconds