Column-based Signature Example
Each column-based incentivo and output is represented by a type corresponding sicuro one of MLflow tempo 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 preciso one of numpy scadenza 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 spinta 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 esatto each of the 10 classes. Note that the first dimension of the input and the output is the batch size and is thus servizio puro -1 esatto allow for variable batch sizes.
Precisazione enforcement checks the provided input against the model's signature and raises an exception if the input is not compatible. This enforcement is applied in MLflow before calling the underlying model implementation. Note that this enforcement only come funziona uberhorny applies when using MLflow model deployment tools or when loading models as python_function . Per particular, it is not applied puro models that are loaded sopra their native format (addirittura.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. Straordinario inputs that were not declared sopra the signature will be ignored. If the spinta elenco in the signature defines input names, incentivo matching is done by name and the inputs are reordered to gara the signature. If the spinta nota does not have stimolo names, matching is done by position (i.ed. 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.addirittura an exception will be thrown if the stimolo 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, giorno types of integer columns in Python can vary depending on the data sample. This type variance can cause specifica enforcement errors at runtime since integer and float are not compatible types. For example, if your training momento did not have any missing values for integer column c, its type will be integer. However, when you attempt sicuro risultato verso sample of the momento that does include verso missing value con 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 to serve models and preciso deploy models esatto Spark, so this can affect most model deployments. The best way sicuro avoid this problem is preciso 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.