def predict(MLModelId=None, Record=None, PredictEndpoint=None):
    """
    Generates a prediction for the observation using the specified ML Model .
    See also: AWS API Documentation
    
    
    :example: response = client.predict(
        MLModelId='string',
        Record={
            'string': 'string'
        },
        PredictEndpoint='string'
    )
    
    
    :type MLModelId: string
    :param MLModelId: [REQUIRED]
            A unique identifier of the MLModel .
            

    :type Record: dict
    :param Record: [REQUIRED]
            A map of variable name-value pairs that represent an observation.
            (string) --The name of a variable. Currently it's used to specify the name of the target value, label, weight, and tags.
            (string) --The value of a variable. Currently it's used to specify values of the target value, weights, and tag variables and for filtering variable values.
            
            

    :type PredictEndpoint: string
    :param PredictEndpoint: [REQUIRED]

    :rtype: dict
    :return: {
        'Prediction': {
            'predictedLabel': 'string',
            'predictedValue': ...,
            'predictedScores': {
                'string': ...
            },
            'details': {
                'string': 'string'
            }
        }
    }
    
    
    :returns: 
    Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD
    PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.
    PredictedScores - Contains the raw classification score corresponding to each label.
    PredictedValue - Present for a REGRESSION MLModel request.
    
    """
    pass
