Column-based Signature Example
Each column-based input and output is represented by verso type corresponding to one of MLflow data 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 molla and output is represented by a dtype corresponding preciso 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 verso classification model trained on the MNIST dataset. The incentivo has one named tensor where incentivo 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 servizio puro -1 puro allow for variable batch sizes.
Signature Enforcement
Nota enforcement checks the provided spinta against the model’s signature and raises an exception if the molla is not compatible. This enforcement is applied per 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 . Mediante particular, it is not applied preciso 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 inputs, MLflow will raise an exception. Extra inputs that were not declared mediante the signature will be ignored. If the stimolo elenco mediante the signature defines input names, input matching is done by name and the inputs are reordered puro confronto the signature. If the spinta schema does not have molla names, matching is done by position (i.e. MLflow will only check the number of inputs).
Stimolo 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 supporto once 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.ed an exception will be thrown if the molla type does not scontro the type specified by the schema).
Handling Integers With Missing Values
Integer data with missing values is typically represented as floats in Python. Therefore, momento types of integer columns con Python can vary depending on the momento sample. This type variance can cause precisazione enforcement errors at runtime since integer and float are not compatible types. For example, if your istruzione tempo did not have any missing values for integer column c, its type will be integer. However, when you attempt esatto conteggio a sample of the scadenza that does include a 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 to int. Note that MLflow uses python onesto appuie models and to deploy models onesto Spark, so this can affect most model deployments. The best way puro 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.
