What Is Cerebrium?
Cerebrium is an advanced AI tool designed to revolutionize the world of machine learning (ML). At its core, it offers a serverless infrastructure that eliminates the complexities of ML setup and maintenance. Developers can now easily train, deploy, and monitor ML models using just a few lines of code, greatly simplifying the ML development process.
One of Cerebrium’s standout features is its serverless GPU model deployment. With a single line of code, users can deploy models from major ML frameworks like PyTorch and ONNX, as well as prebuilt models optimized for sub-second latency. This streamlined deployment process ensures that models are up and running swiftly, minimizing the time and effort required.
Cerebrium also prioritizes user convenience with automatic versioning. Each time a model is deployed, it is automatically versioned, enabling easy rollbacks to previous versions with a simple click. This feature simplifies model management and provides users with greater control and flexibility in their ML workflows.
Cerebrium’s architecture is optimized for rapid cold starts, even for demanding language models like Flan-T5 and GPT Neox, which can start in less than one second. The platform leverages powerful Nvidia GPUs, including T4’s, A10’s, and A100’s, while charging users only for their inference time, ensuring cost-efficiency.
Cerebrium offers a beta feature for effortless fine-tuning of smaller models on specific tasks. This capability allows users to reduce costs, decrease latency, and enhance overall model performance with minimal code changes, eliminating the need to worry about infrastructure management.
Cerebrium doesn’t stop at deployment; it simplifies model monitoring and management as well. Users can seamlessly integrate with leading ML observability platforms, set up monitors and alerts, detect feature or prediction drift, compare model versions, and gain insights into the factors contributing most to model performance.
Cerebrium empowers ML practitioners to focus on model development and optimization while streamlining the complexities of deployment, versioning, and monitoring. It offers a comprehensive suite of tools to make the entire ML lifecycle more accessible and efficient.
