DKube supports an end-to-end MLOps workflow from feature engineering through production deployment. The platform is based on the popular Kubeflow framework, bringing together its powerful components and enhancing them with best-in-class capabilities such as:
- Feature Engineering
- Tracking and Lineage
- MLFlow-based metric collection and compare
- Flexible data source integration
- CI/CD-based automation
This is all integrated into a flexible, UI-based workflow. It is intuitive enough to allow team members to be collaborating on real research within hours of starting the installation.
DKube is optimized for on-prem installation out-of-the-box. The difficult, time-consuming task of integrating the hardware with the software components is handled by our Helm-based installation. Because it runs on top of Kubernetes, DKube works with the same look, feel, workflow, and reliability on a cloud-based platform. And your work can be quickly and easily migrated back and forth.
DKube is standards-based from the ground up. It uses the best-in-class frameworks and tools, including:
- Scikit Learn
And it supports the most common authorization standards: GitHub & LDAP.
Flexible code and data integration is built into the workflow. It supports:
- The most popular code repositories, including GitHub, GitLab, and Bitbucket
- The most common storage standards for data and models, including GitHub, GitLab, Bitbucket, AWS S3, Minio, Google Cloud Storage, and Redshift.