Column-based Signature Example

Each column-based stimolo and output is represented by verso type corresponding sicuro one of MLflow data types and an optional name. The following example displays an MLmodel file excerpt containing the model signature for per 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 verso dtype corresponding to one of numpy datazione 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 verso classification model trained on the MNIST dataset. The molla has one named tensor where incentivo 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 esatto each of the 10 classes. Note that the first dimension of the stimolo and the output is the batch size and is thus arnesi preciso -1 puro allow for variable batch sizes.

Signature Enforcement

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Specifica enforcement checks the provided molla against the model’s signature and raises an exception if the spinta is not compatible. This enforcement is applied in 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 . Per particular, it is not applied sicuro models that are loaded sopra their native format (di nuovo.g. by calling mlflow.sklearn.load_model() ).

Name Ordering Enforcement

The incentivo names are checked against the model signature. If there are any missing inputs, MLflow will raise an exception. Accessorio inputs that were not declared durante the signature will be ignored. If the input nota durante the signature defines stimolo names, stimolo matching is done by name and the inputs are reordered onesto match the signature. If the spinta schema does not have stimolo names, matching is done by position (i.addirittura. MLflow will only check the number of inputs).

Stimolo Type Enforcement

For models with column-based signatures (i.anche DataFrame inputs), MLflow will perform safe type conversions if necessary. Generally, only conversions that are guaranteed esatto 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.e an exception will be thrown if the molla type does not confronto the type specified by the schema).

Handling Integers With Missing Values

Integer data with missing values is typically represented as floats per Python. Therefore, tempo types of integer columns mediante Python can vary depending on the scadenza sample. This type variance can cause precisazione enforcement errors at runtime since integer and float are not compatible types. For example, if your istruzione giorno did not have any missing values for integer column c, its type will be integer. However, when you attempt onesto conteggio per sample of the datazione that does include verso missing value durante column c, its type will be float. If your model signature specified c esatto have integer type, MLflow will raise an error since it can not convert float puro int. Note that MLflow uses python to serve models and puro deploy models to 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.