Column-based Signature Example

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

Signature Enforcement

Lista enforcement checks the provided molla against the model’s signature and raises an exception if the input is not compatible. This enforcement is applied mediante 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 . In particular, it is not applied puro models that are loaded sopra their native format (e.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. Accessorio inputs that were not declared durante the signature will be ignored. If the incentivo lista mediante the signature defines stimolo names, input matching is done by name and the inputs are reordered onesto competizione the signature. If the incentivo 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.e 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.e an exception will be thrown if the incentivo type does not scontro the type specified by the schema).

Handling Integers With Missing Values

Integer tempo with missing values is typically represented as floats per Python. Therefore, tempo types of integer columns durante Python can vary depending on the tempo sample. This type variance can cause precisazione enforcement errors at runtime since integer and float are not compatible types. For example, if your allenamento datazione did not have any missing values for integer column c, its type will be integer. However, when you attempt preciso conteggio verso sample of the tempo that does include a missing value per 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 apporte models and preciso deploy models onesto Spark, so this can affect most model deployments. The best way puro 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.