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

Column-based Signature Example

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

Tensor-based Signature Example

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

Model Incentivo Example

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

How Puro Log Model With Column-based Example

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

How Puro 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 mediante the model signature. The sample molla can be passed durante as verso numpy ndarray or a dictionary mapping a string onesto a numpy array. The following example demonstrates how you can log per tensor-based spinta example with your model:

Model API

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

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

Built-Durante Model Flavors

MLflow provides several canone flavors that might be useful per your applications. Specifically, many of its deployment tools support these flavors, so you can export your own model per one of these flavors preciso benefit from all these tools:

Python Function ( python_function )

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

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

How Esatto 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 mediante the exported models. In prime, 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 esatto ML models. For more information, see the custom Python models documentation .