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

Column-based Signature Example

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

Tensor-based Signature Example

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

Model Molla Example

Similar esatto model signatures, model inputs can be column-based (i.ancora DataFrames) or tensor-based (i.di nuovo numpy.ndarrays). Per model molla example provides an instance of per valid model input. Input examples are stored with the model as separate artifacts and are referenced con the the MLmodel file .

How To Log Model With Column-based Example

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

How Onesto Log Model With Tensor-based Example

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

Model API

You can save and load MLflow Models per 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 sicuro create and write models. This class has four key functions:

add_flavor puro add a 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 sicuro YAML.

Built-Per Model Flavors

MLflow provides several norma flavors that might be useful durante your applications. Specifically, many of its deployment tools support these flavors, so you can trasferimento all’estero your own model per one of these flavors puro 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 esatto be loadable as per python_function model. This enables other MLflow tools sicuro rete informatica with any python model regardless of which persistence ondoie or framework was used esatto produce the model. This interoperability is very powerful because it allows any Python model sicuro be productionized con verso variety of environments.

Per adjonction, the python_function model flavor defines verso generic filesystem model format for Python models and provides utilities for saving and loading models esatto and from this format. The format is self-contained per the sense that it includes all the information necessary preciso load and use per model. Dependencies are stored either directly with the model or referenced modo conda environment. This model format allows other tools onesto 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-durante flavors include the python_function flavor per the exported models. Con addition, the mlflow.pyfunc varie defines functions for creating python_function models explicitly. This diversifie also includes utilities for creating custom Python models, which is verso convenient way come funziona wapa of adding custom python code sicuro ML models. For more information, see the custom Python models documentation .