solutions
Automation
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-
Integrate DKube into your existing product:
Feature Engineering
Tracking and Lineage
MLFlow-based metric collection and compare
Flexible data source integration
CI/CD-based automation
Workflow
All of this 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.
Frameworks
DKube is standards-based from the ground up. It uses the best-in-class frameworks and tools, including:
TensorFlow
PyTorch
Scikit Learn
JupyterLab
RStudio
Katib
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Also 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.
