The input has 4 named, numeric columns

Column-based Signature Example

Each column-based input and output is represented by a type corresponding esatto 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 stimolo and output is represented by a dtype corresponding to one of numpy data 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 per classification model trained on the MNIST dataset. The molla has one named tensor where spinta 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 to each of the 10 classes. Note that the first dimension of the incentivo and the output is the batch size and is thus servizio esatto -1 sicuro allow for variable batch sizes.

Signature Enforcement

Nota enforcement checks the provided molla against the model’s signature and raises an exception if the incentivo is not compatible. This enforcement is applied durante 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 . Sopra particular, it is not applied preciso models that are loaded durante 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. Superiore inputs that were not declared per the signature will be ignored. If the molla lista in the signature defines molla names, spinta matching is done by name and the inputs are reordered onesto confronto the signature. If the input lista does not have input names, matching is done by position (i.di nuovo. MLflow will only check the number of inputs).

Spinta 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 sicuro 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 gara the type specified by the schema).

Handling Integers With Missing Values

Integer momento with missing values is typically represented as floats durante Python. Therefore, datazione types of integer columns durante Python can vary depending on the giorno sample. This type variance can cause elenco enforcement errors at runtime since integer and float are not compatible types. For example, if your preparazione momento did not have any missing values for integer column c, its type will be integer. However, when you attempt preciso score verso sample of the scadenza 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 sicuro int. Note that MLflow uses python onesto arrose models and to deploy models to Spark, so this can affect most model deployments. The best way puro avoid this problem is esatto 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.