The model configuration is included in the process of uploading models to Openlayer. It is usually provided as a dictionary/object or as a YAML file. Refer to the API reference for details on the upload process.

Note that the notion of uploading a model only applies to development projects. If you are working with monitoring, you only need to upload/stream production data.


For tabular regression models, the config can contain the following attributes. Note that not all attributes are required, as some contain default values.

architectureTypestr“custom”The model’s framework.Must be one of: sklearn, xgboost, fasttext, keras, pytorch, rasa, tensorflow, transformers, llm. If the framework being used is not one of the above, use custom.
categoricalFeatureNamesList[str][]A list containing the names of all categorical features used by the model.E.g. [“Gender”, “Geography”].
featureNamesList[str][]List of input feature names.
metadataDict[str, any]{}Dictionary containing metadata about the model.This is the metadata that will be displayed on the Openlayer platform.
namestr“Model”Name of the model.


Let’s look at an example dataset from one of the sample notebooks from Openlayer’s examples gallery GitHub repository.

A valid model_config.yaml file would be:

architectureType: sklearn
  - age
  - sex
  - bmi
  - bp
  - s1
  - s2
  - s3
  - s4
  - s5
  - s6
  model_type: Linear Regression
  regularization: None
name: Diabetes prediction model