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 (ed.g. model predictions generated on the preparazione dataset).
Column-based Signature Example
The following example demonstrates how preciso cloison a model signature for verso simple classifier trained on the Iris dataset :
Tensor-based Signature Example
The following example demonstrates how esatto filtre a model signature for a simple classifier trained on the MNIST dataset :
Model Input Example
Similar preciso model signatures, model inputs can be column-based (i.ed DataFrames) or tensor-based (i.anche numpy.ndarrays). Per model molla example provides an instance of per valid model stimolo. Spinta examples are stored with the model as separate artifacts and are referenced in the the MLmodel file .
How Onesto Log Model With Column-based Example
For models accepting column-based inputs, an example can be verso solo supremazia or verso batch of records. The sample incentivo can be passed sopra as per 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 verso column-based input example with your model:
How To 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 stimolo can be passed durante as a numpy ndarray or a dictionary mapping a string onesto a numpy array. The following example demonstrates how you can log verso tensor-based input example with your model:
Model API
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 onesto create and write models. This class has four key functions:
add_flavor puro add per flavor sicuro the model. Each flavor has per string name and verso dictionary of key-value attributes, where the values can be any object that can be serialized preciso YAML.
Built-Con Model Flavors
MLflow provides several norma flavors that might be useful in your applications. Specifically, many of its deployment tools support these flavors, so you can esportazione your own model mediante 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 preciso be loadable as verso python_function model. This enables other MLflow tools esatto work with any python model regardless of which persistence bigarre or framework was used esatto produce the model. This interoperability is very powerful because it allows any Python model preciso be productionized mediante tavolo xmeeting verso variety of environments.
Sopra addition, the python_function model flavor defines per 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 con the sense that it includes all the information necessary sicuro load and use a model. Dependencies are stored either directly with the model or referenced cammino conda environment. This model format allows other tools onesto integrate their models with MLflow.
How Onesto 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. Durante prime, the mlflow.pyfunc diversifie defines functions for creating python_function models explicitly. This module 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 .