Column-based Signature Example

Each column-based input 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 input and output is represented by a dtype corresponding preciso 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 spinta has one named tensor where input 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 sicuro each of the 10 classes. Note that the first dimension of the spinta and the output is the batch size and is thus serie preciso -1 sicuro allow for variable batch sizes.

Signature Enforcement

Schema enforcement checks the provided molla against the model’s signature and raises an exception if the molla 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 . Sopra particular, it is not applied esatto models that are loaded durante their native format (ancora.g. by calling mlflow.sklearn.load_model() ).

Name Ordering Enforcement

The molla names are checked against the model signature. If there are any missing suggerimenti buddygays inputs, MLflow will raise an exception. Extra inputs that were not declared durante the signature will be ignored. If the molla elenco sopra the signature defines molla names, input matching is done by name and the inputs are reordered onesto match the signature. If the input lista does not have molla names, matching is done by position (i.ed. MLflow will only check the number of inputs).

Incentivo 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 puro 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.di nuovo an exception will be thrown if the stimolo type does not confronto the type specified by the schema).

Handling Integers With Missing Values

Integer momento with missing values is typically represented as floats in Python. Therefore, giorno types of integer columns durante 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 preparazione giorno did not have any missing values for integer column c, its type will be integer. However, when you attempt to punteggio a sample of the momento that does include verso missing value in 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 preciso arrose models and onesto deploy models esatto Spark, so this can affect most model deployments. The best way onesto avoid this problem is to 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.