How to launch a single model training or data preprocessing run through the DKube UI while passing new parameters to pass to the training program via the UI and designated environment variables. Learn how to track the performance characteristics and the lineage of the run with the dataset and model versions used or created in the process.
Cloud-based computing has enabled organizations to make use of high-performance resources without requiring large IT groups. And it has enabled a supply of production-ready applications to companies who might not otherwise be able to access them. But, what if your organization can’t make use of the public cloud?
How to deploy a preferred version of a model into production or to first push it to a model catalog for another gatekeeper to test and select a preferred model before pushing into production. The model catalog capability is unique to DKube and minimizes accidental escape to production of a model that may not be ready yet.
Reproducibility is the foundational principle of the scientific method. If an experiment cannot be repeated, it is assumed to be faulty. DKube, an end-to-end Kubeflow-based MLOps platform, offers complete reproducibility into an integrated workflow. Without the ability to trace and repeat your work, it is not science
There's a faster way to go from research to application. Find out how an MLOps workflow can benefit your teams.