Column-based Signature Example

Each column-based spinta and output is represented by per type corresponding onesto one of MLflow datazione 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 onesto 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 a classification model trained on the MNIST dataset. The incentivo 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 puro each of the 10 classes. Note that the first dimension of the input and the output is the batch size and is thus set preciso -1 puro allow for variable batch sizes.

Signature Enforcement

Precisazione enforcement checks the provided incentivo against the model’s signature and raises an exception if the stimolo 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 . Durante particular, it is not applied onesto models that are loaded per their native format (anche.g. by calling mlflow.sklearn.load_model() ).

Name Ordering Enforcement

The stimolo names are checked against the model signature. If there are any missing inputs, MLflow will raise an exception. Extra inputs that were not declared con the signature will be ignored. If the input specifica mediante the signature defines spinta names, spinta matching is done by name and the inputs are reordered esatto scontro the signature. If the stimolo nota does not have spinta names, matching is done by position (i.anche. MLflow will only check the number of inputs).

Input Type Enforcement

For models with column-based signatures (i.di nuovo 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.anche 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 per Python. Therefore, scadenza types of integer columns con Python can vary depending on the scadenza sample. This type variance can cause elenco 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 esatto risultato per sample of the datazione that does include verso missing value per 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 to arrose models and to deploy models onesto Spark, so this can affect most model deployments. The best way preciso avoid this problem is puro 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.