MLForecast: Create a MLForecast object with pre-trained models for inference deployments

[pljnagda]

Description

Allow creation of a MLForecast with pre-trained models.

class MLForecast:
    def __init__(
        self,
        models: Models,
        freq: Optional[Freq] = None,
        lags: Optional[Lags] = None,
        lag_transforms: Optional[LagTransforms] = None,
        date_features: Optional[Iterable[DateFeature]] = None,
        differences: Optional[Iterable[int]] = None,
        num_threads: int = 1,
        target_transforms: Optional[List[BaseTargetTransform]] = None,
        is_models_pretrained: bool: = False,
    ):

Use case

Example use case would be a real time inference deployment where each server needs to create a MLForecast object on startup for inference without the need to fit the models.

[jmoralez]

Hey @pljnagda, we usually recommend using pickle (example). Would that not work for your use case?

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