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.
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.
Over the past two plus years Kubeflow has come together a long way in bringing together many open source innovations to implement a cost effective AI/ML platform. These include KF Pipelines, KF Serving, AutoML, Istio from Kubernetes in addition to framework operators like Tensorflow, PyTorch, XGBoost, SciKit. However, “implementing” Kubeflow with your preferred cloud or on-prem environment still requires significant work still, with few people, for many months. The work comes in terms of: