Optional content_The upload state.
Optional customUser defined properties specified as key-value pairs.
Optional data_An optional array which contains the data preprocessing transformations that were executed by the training job that created this model.
Optional domainUser provided domain name for this model. For example: sentiment, entity, visual-recognition, finance, retail, real estate etc.
Optional hybrid_The list of the software specifications that are used by the pipeline that generated this model, if the model was generated by a pipeline.
Optional hyper_Hyper parameters used for training this model.
Optional label_The name of the label column.
Optional metricsMetrics that can be returned by an operation.
Optional model_The model definition.
Optional model_Optional metadata that can be used to provide information about this model that can be tracked with IBM AI Factsheets. See Using AI Factsheets for more details.
Optional pipelineA reference to a resource.
Optional schemasIf the prediction schemas are provided here then they take precedent over any schemas provided in the data references. Note that data references contain the schema for the associated data and this object contains the schema(s) for the associated prediction, if any. In the case that the prediction input data matches exactly the schema of the training data references then the prediction schema can be omitted. However it is highly recommended to always specify the prediction schemas using this field.
Optional sizeThis will be used by scoring to record the size of the model.
Optional software_A software specification.
Optional test_The holdout/test datasets.
Optional trainingInformation about the training job that created this model.
Optional training_The training data that was used to create this model.
Optional training_Deprecated: this is replaced by training.id. This field can be used to store the id of the training job
that was used to produce this model.
Optional transformed_The name of the label column seen by the estimator, which may have been transformed by the previous
transformers in the pipeline. This is not necessarily the same column as the label_column in the initial data
set.
The model type. The supported model types can be found in the documentation here.
Optional user_User defined objects referenced by the model. For any user defined class or function used in the model, its
name, as referenced in the model, must be specified as the key and its fully qualified class or function name
must be specified as the value. This is applicable for Tensorflow 2.X models serialized in H5 format using
the tf.keras API.
Information related to the upload of the model content.