Esatto include verso signature with your model, pass signature object as an argument onesto the appropriate log_model call, addirittura

g. sklearn.log_model() . The model signature object can be created by hand or inferred from datasets with valid model inputs (e.g. the istruzione dataset with target column omitted) and valid model outputs (anche.g. model predictions generated on the addestramento dataset).

Column-based Signature Example

The following example demonstrates how onesto filtre per model signature for a simple classifier trained on the Iris dataset :

Tensor-based Signature Example

The following example demonstrates how sicuro store verso model signature for a simple classifier trained on the MNIST dataset :

Model Input Example

Similar onesto model signatures, model inputs can be column-based (i.ancora DataFrames) or tensor-based (i.addirittura numpy.ndarrays). A model input example provides an instance of per valid model spinta. Molla examples are stored with the model as separate artifacts and are referenced per the the MLmodel file .

How Esatto Log Model With Column-based Example

For models accepting column-based inputs, an example can be per celibe superiorita or verso batch of records. The sample molla can be passed sopra as verso Pandas DataFrame, list or dictionary. The given example will be converted preciso verso Pandas DataFrame and then serialized preciso json using the Pandas split-oriented format. Bytes are base64-encoded. The following example demonstrates how you can log per column-based stimolo example with your model:

How Sicuro Log Model With Tensor-based Example

For models accepting tensor-based inputs, an example must be per batch of inputs. By default, the axis 0 is the batch axis unless specified otherwise per the model signature. The sample stimolo can be passed durante as a numpy ndarray or a dictionary mapping nome utente seniorblackpeoplemeet per string puro verso numpy array. The following example demonstrates how you can log per tensor-based incentivo example with your model:

Model API

You can save and load MLflow Models mediante multiple ways. First, MLflow includes integrations with several common libraries. For example, mlflow.sklearn contains save_model , log_model , and load_model functions for scikit-learn models. Second, you can use the mlflow.models.Model class esatto create and write models. This class has four key functions:

add_flavor puro add verso flavor preciso the model. Each flavor has per string name and a dictionary of key-value attributes, where the values can be any object that can be serialized esatto YAML.

Built-Sopra Model Flavors

MLflow provides several canone flavors that might be useful con your applications. Specifically, many of its deployment tools support these flavors, so you can commercio internazionale your own model sopra one of these flavors onesto benefit from all these tools:

Python Function ( python_function )

The python_function model flavor serves as per default model interface for MLflow Python models. Any MLflow Python model is expected onesto be loadable as a python_function model. This enables other MLflow tools onesto work with any python model regardless of which persistence ondule or framework was used esatto produce the model. This interoperability is very powerful because it allows any Python model puro be productionized sopra verso variety of environments.

In addenda, the python_function model flavor defines per generic filesystem model format for Python models and provides utilities for saving and loading models preciso and from this format. The format is self-contained mediante the sense that it includes all the information necessary puro load and use per model. Dependencies are stored either directly with the model or referenced modo conda environment. This model format allows other tools esatto integrate their models with MLflow.

How Sicuro Save Model As Python Function

Most python_function models are saved as part of other model flavors – for example, all mlflow built-per flavors include the python_function flavor mediante the exported models. Durante addition, the mlflow.pyfunc ondule defines functions for creating python_function models explicitly. This bigarre also includes utilities for creating custom Python models, which is per convenient way of adding custom python code preciso ML models. For more information, see the custom Python models documentation .