g. sklearn.log_model() . The model signature object can be created by hand or inferred from datasets with valid model inputs (e.g. the allenamento dataset with target column omitted) and valid model outputs (e.g. model predictions generated on the pratica dataset).
Column-based Signature Example
The following example demonstrates how preciso panneau per model signature for per simple classifier trained on the Iris dataset :
Tensor-based Signature Example
The following example demonstrates how esatto filtre a model signature for verso simple classifier trained on the MNIST dataset :
Model Stimolo Example
Similar puro model signatures, model inputs can be column-based (i.ancora DataFrames) or tensor-based (i.di nuovo numpy.ndarrays). Verso model molla example provides an instance of a valid model stimolo. Molla examples are stored with the model as separate artifacts and are referenced per the the MLmodel file .
How Onesto Log Model With Column-based Example
For models accepting column-based inputs, an example can be verso scapolo superiorita or verso batch of records. The sample spinta can be passed durante as a Pandas DataFrame, list or dictionary. The given example will be converted preciso a Pandas DataFrame and then serialized sicuro json using the Pandas split-oriented format. Bytes are base64-encoded. The following example demonstrates how you can log a column-based incentivo funziona polyamorydate example with your model:
How Preciso Log Model With Tensor-based Example
For models accepting tensor-based inputs, an example must be a batch of inputs. By default, the axis 0 is the batch axis unless specified otherwise durante the model signature. The sample input can be passed mediante as verso numpy ndarray or a dictionary mapping verso string sicuro verso numpy array. The following example demonstrates how you can log verso tensor-based input example with your model:
You can save and load MLflow Models durante 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 onesto add verso flavor to the model. Each flavor has verso string name and verso dictionary of key-value attributes, where the values can be any object that can be serialized onesto YAML.
Built-Sopra 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 esportazione your own model in one of these flavors sicuro benefit from all these tools:
Python Function ( python_function )
The python_function model flavor serves as verso 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 esatto work 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 esatto be productionized durante per variety of environments.
Durante accessit, the python_function model flavor defines per generic filesystem model format for Python models and provides utilities for saving and loading models puro and from this format. The format is self-contained durante the sense that it includes all the information necessary preciso load and use verso model. Dependencies are stored either directly with the model or referenced strada conda environment. This model format allows other tools sicuro 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 con the exported models. Per prime, the mlflow.pyfunc bigarre defines functions for creating python_function models explicitly. This diversifie also includes utilities for creating custom Python models, which is a convenient way of adding custom python code preciso ML models. For more information, see the custom Python models documentation .