Get started building models even faster by using Public Checkpoints.

trainML now supports the creation and use of Checkpoints to store immutable versions of large model weight files.

Google Drive files can now be downloaded directly into trainML Datasets and Models.

Analytics providers can now run their models directly on the trainML deployments of their customers. This allows the analytics provider to maintain and protect their intellectual property while providing analytics services inside their customers' secure, private infrastructure.

Run real-time inference workloads on NVIDIA Jetson fully managed by CloudBender™.

Physical CloudBender™ regions now support running a centralized storage controller similar to cloud regions.

Integration with Azure Blob Storage and Azure Container Registry is now available natively in trainML.

CloudBender™ now allows you deploy applications as endpoints to your local region, so they are only accessible from inside your infrastructure.

Customers can now disable the automatic archiving of job outputs prior to upload.

Wasabi cloud storage has been added as an available storage integration. Wasabi can save you up to 80% on persistent storage compared to AWS and has no additional egress/API fees, making it a great option for trainML integration.