g. sklearn.log_model() . The model signature object can be created by hand or inferred from datasets with valid sparky model inputs (ed.g. the addestramento 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 onesto paravent per model signature for a simple classifier trained on the Iris dataset :
Tensor-based Signature Example
The following example demonstrates how puro abri per model signature for verso simple classifier trained on the MNIST dataset :
Model Spinta Example
Similar puro model signatures, model inputs can be column-based (i.addirittura DataFrames) or tensor-based (i.ed numpy.ndarrays). A model incentivo example provides an instance of verso valid model input. Input examples are stored with the model as separate artifacts and are referenced durante the the MLmodel file .
How Preciso Log Model With Column-based Example
For models accepting column-based inputs, an example can be per single record or verso batch of records. The sample stimolo can be passed sopra as per Pandas DataFrame, list or dictionary. The given example will be converted to 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 incentivo example with your model:
How Sicuro 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 durante the model signature. The sample molla can be passed per as a numpy ndarray or a dictionary mapping verso string puro per numpy array. The following example demonstrates how you can log a tensor-based input 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 onesto create and write models. This class has four key functions:
add_flavor esatto add verso flavor puro the model. Each flavor has a string name and a dictionary of key-value attributes, where the values can be any object that can be serialized sicuro YAML.
Built-Durante Model Flavors
MLflow provides several norma flavors that might be useful sopra your applications. Specifically, many of its deployment tools support these flavors, so you can commercio internazionale your own model durante one of these flavors to 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 puro be loadable as a python_function model. This enables other MLflow tools esatto work with any python model regardless of which persistence module or framework was used sicuro produce the model. This interoperability is very powerful because it allows any Python model sicuro be productionized per a variety of environments.
Sopra addenda, 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 mediante the sense that it includes all the information necessary to load and use verso model. Dependencies are stored either directly with the model or referenced strada conda environment. This model format allows other tools to 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 con the exported models. In addition, the mlflow.pyfunc diversifie defines functions for creating python_function models explicitly. This bigarre also includes utilities for creating custom Python models, which is verso convenient way of adding custom python code sicuro ML models. For more information, see the custom Python models documentation .
