SageMaker-like service for private clouds

Let your data scientists leverage the power of Kubeflow, Ray and MLflow without the hassle of managing the underlying infrastructure and the software in public clouds and in your private data center. Eliminate the operational complexity associated with infrastructure and software lifecycle management.

Provide data scientists and developers with a unified, consistent interface regardless of the underlying infrastructure, simplifying training, development, and operational processes.

Streamline your ML workflows with seamless integration from data ingestion to model deployment and monitoring, all within a single, cohesive solution.

Allow ML environment customization to suit specific requirements, including support for different machine learning platforms (Kubeflow, MLflow and Ray), frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.

Platform teams deliver much-needed capabilities to data scientists as a service, while having the ability to manage, monitor, and secure environments according to their organization’s policies. This includes control over updates, patches, and system configurations.