We started by addressing the problem of model management: how to track, version, and audit models used across products, based on our research at MIT CSAIL building ModelDB – an open-source model management system deployed at multiple Fortune 500 companies – to create the Verta platform. Today, Verta provides model management and operations solutions for the entire AI & ML model lifecycle, from experiment tracking and production registry to deployment, inference & serving, and monitoring.
Enterprise pricing delivered as SaaS, on-prem, or VPC.
Verta is a Kubernetes-based platform. Itcan deployed on any Kubernetes cluster, whether in the cloud or on-premise. Verta is offered as a hosted service managed by Verta or an enterprise deployment on-premise or in a VPC.
Verta platform is agnostic to any ML framework and Data prep/ML training platforms and integrates with them using a lightweight client library. This includes: Tensorflow, Pytorch, Sklearn, XGBoost, R, Spark, ONNX, PMML, Kubeflow, Anaconda, Spark, Sagemaker, AWS, VMware, Azure, Google CloudAdditionally we integrate with all the standard DevOps tools within the enterprise ecosystem. For example Github, PyPI, Jenkins, Kafka, Datadog etc.
The platform supports a robust RBAC (Role Based Access Control) system and integrates into enterprise identity systems (e.g Okta, Active Directory, etc.) along with the ability to define roles and permissions, and to audit activity on the platform. You can set up SSO (Single Sign On) and automate user provisioning with your enterprise identity provider.
We have different levels of deployment options to choose from starting from a fully managed SaaS service to fully air-gapped deployment. In the middle we also have a managed service option where we can push updates and manage deployment and have access to operational metrics only to provide 24X7 support without any access to your model at all.
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