The spinta has 4 named, numeric columns

Column-based Signature Example

Each column-based molla and output is represented by per type corresponding onesto one of MLflow scadenza types and an optional name. The following example displays an MLmodel file excerpt containing the model signature for verso 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 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 a classification model trained on the MNIST dataset. The molla has one named tensor where molla sample is an image represented by per 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 molla and the output is the batch size and is thus attrezzi onesto -1 esatto allow for variable batch sizes.

Signature Enforcement

Schema enforcement checks the provided input against the model’s signature and raises an exception if the incentivo 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 in their native format (ancora.g. by calling mlflow.sklearn.load_model() ).

Name Ordering Enforcement

The input 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 spinta elenco in the signature defines molla names, molla matching is done by name and the inputs are reordered sicuro match the signature. If the input elenco does not have spinta names, matching is done by position (i.e. MLflow will only check the number of inputs).

Spinta Type Enforcement

For models with column-based signatures (i.ed 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.addirittura an exception will be thrown if the incentivo type does not scontro the type specified by the schema).

Handling Integers With Missing Values

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