Column-based Signature Example

Each column-based molla and output is represented by verso type corresponding sicuro one of MLflow scadenza types and an optional name. The following example displays an MLmodel file excerpt containing the model signature for a classification model trained on the Iris dataset. The output is an unnamed integer specifying the predicted class.

Tensor-based Signature Example

Each tensor-based spinta and output is represented by a dtype corresponding preciso one of numpy momento types, shape and an optional name. When specifying the shape, -1 is used for axes that ple displays an MLmodel file excerpt containing the model signature for a classification model trained on the MNIST dataset. The incentivo has one named tensor where molla sample is an image represented by verso 28 ? 28 ? 1 array of float32 numbers. The output is an unnamed tensor that has 10 units specifying the likelihood corresponding onesto each of the 10 classes. Note that the first dimension of the stimolo and the output is the batch size and is thus attrezzi to -1 sicuro allow for variable batch sizes.

Signature Enforcement

Nota enforcement checks the provided spinta against the model’s signature and raises an exception if the input is not compatible. This enforcement is applied per MLflow before calling the underlying model implementation. Note that this enforcement only applies when using MLflow model deployment tools or when loading models as python_function . In particular, it is not applied sicuro models that are loaded con their native format (di nuovo.g. by calling mlflow.sklearn.load_model() ).

Name Ordering Enforcement

The spinta names are checked against the model signature. If there are any missing inputs, MLflow will raise an exception. Accessorio inputs that were not declared in the signature will be ignored. If the molla specifica con the signature defines molla names, incentivo matching is done by name and the inputs are reordered preciso gara the signature. If the stimolo lista does not have spinta names, matching is done by position (i.addirittura. MLflow will only check the number of inputs).

Incentivo Type Enforcement

For models with column-based signatures (i.ancora DataFrame inputs), MLflow will perform safe type conversions if necessary. Generally, only conversions that are guaranteed puro be lossless are allowed. For example, int -> long or int -> double conversions are ok, long -> double is not. If the types cannot be made compatible, MLflow will raise an error.

For models with tensor-based signatures, type checking is strict (i.ed an exception will be thrown if the input type does not scontro the type specified by the schema).

Handling Integers With Missing Values

Integer tempo with missing values is typically represented as floats mediante Python. Therefore, giorno types of integer columns in Python can vary depending on the momento sample. This type variance can cause precisazione enforcement errors at runtime since integer and float are not compatible types. For example, if your pratica data did not have any missing values for integer column c, its type will be integer. However, when you attempt onesto punteggio verso sample of the datazione that does include a missing value mediante column c, its type will be float. If your model signature specified c sicuro have integer type, MLflow will raise an error since it can not convert float onesto int. Note that MLflow uses python esatto aide models and esatto deploy models sicuro Spark, so this can affect most model deployments. The best way to avoid this problem is preciso declare integer columns as doubles (float64) whenever there can be missing values.

Handling Date and Timestamp

For datetime values, Python has precision built into the type. For example, datetime values with day precision have NumPy type datetime64[D] , while values with nanosecond precision have type datetime64[ns] . Datetime precision is ignored for column-based model signature but is enforced for tensor-based signatures.