Deploying a model to production is only step 1, as models require iterative improvement and ongoing updates. Differences between versions may be ad hoc schema changes or retraining on a new data set to refit the model hyperparameters under the same given schema.
The Superwise platform supports model versions so you can compare and contrast model behavior across any set of versions. You may toggle between model versions, but only one version per model on any given date is enforced.
🚧 Important note
Currently, the platform supports only one active version at a time. The same model can have different versions over time, but only one can be active at any moment.
Version's dataset file size limitation
The sum of the size of all baseline data files together should be up to 100MB
Because each model version could introduce new input formats, each version requires an explicit schema definition. Schema is a collection of different data entities (a.k.a columns) that are part of the specific version of the machine learning decision process. Each data entity has its data type and specific role in the ML process.
To support different relevant pieces in the ML decision process, Superwise supports the following roles:
Each data entity has its data format. Based on data type, our platform calculates the relevant metrics per data entity to provide you with full observability. For example, categorical entities will automatically be computed for entropy while variance will be computed for numeric entities (to view the full set of metrics according to each data type, see here).
Updated 1 day ago