Store the necessary informations

Let's take a look at the declaration of the ModelInfo class:

class ModelInfo(BaseModel):
    name: str
    target: Column
    model_id: str
    encoder_ids: Dict[str, str]
    time_components: List[str]
    feature_types: List[Column]

    encrypted_fields: List[str] = Field(
        default=["target", "encoder_ids", "time_components", "feature_types"],
        exclude=True,
    )

    model: Union[LinearRegression, LogisticRegression] = Field(
        default=None, exclude=True
    )
    encoders: Dict[str, LabelEncoder] = Field(default=None, exclude=True)

The only ones that are required are name, target, model_id, encoder_ids, time_components, and feature_types. As for the rest:

• encrypted_fields describes what you need to encrypt if encrypted is set to true in config.ini. See Appendix C for more info.
• model is the actual machine learning model that you just trained. During prediction, you should be using this.
• encoders is the actual encoders. During prediction, you should be using this.

Let's get back to the save method:

        model_info = cls(
            name=name,
            target=target,
            model_id=model_id,
            encoder_ids=saved_encoders,
            time_components=time_components,
            feature_types=feature_types,
        )

Notice how we only fill in the required fields, and left the rest as default.

What should I include in ModelInfo?

Rule of thumb is to include anything you will need for prediction. That could include the following:

• A portion of the training dataframe
• Some more feature engineering artifacts
• Columns that require additional processing

What should I implement?

The developer of the model algorithm should implement the following:

1. A way to dump the things into an object that can be downloaded (see dump_to_base64 method)
2. The delete method, when you delete a ModelInfo you should also remove the relevant machine learning model and feature engineering artifacts