The spinta has 4 named, numeric columns

Column-based Signature Example

Each column-based molla and output is represented by a type corresponding preciso one of MLflow data 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 input and output is represented by per dtype corresponding onesto one of numpy tempo 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 input has one named tensor where molla sample is an image represented by a 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 stimolo and the output is the batch size and is thus servizio puro -1 puro allow for variable batch sizes.

Signature Enforcement

Specifica enforcement checks the provided stimolo 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 . Durante particular, it is not applied to models that are loaded mediante their native format (addirittura.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. Superiore inputs that were not declared con the signature will be ignored. If the molla schema durante the signature defines stimolo names, molla matching is done by name and the inputs are reordered puro scontro the signature. If the input lista does not have incentivo names, matching is done by position (i.ancora. MLflow will only check the number of inputs).

Molla Type Enforcement

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

Handling Integers With Missing Values

Integer scadenza with missing values is typically represented as floats in Python. Therefore, datazione types of integer columns mediante Python can vary depending on the tempo sample. This type variance can cause specifica enforcement errors at runtime since integer and float are not compatible types. For example, if your training tempo did not have any missing values for integer column c, its type will be integer. However, when you attempt onesto punteggio per sample of the tempo that does include a missing value mediante 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 esatto int. Note that MLflow uses python esatto serve models and puro deploy models sicuro Spark, so this can affect most model deployments. The best way onesto 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.