Model

A Model, in Alida, collects metadata and files implementing a machine learning model.

Management of Models

The Models management page is accessible from the sidebar menu:

^{View models from the sidebar menu}

From there, it is possible to view the list of Models available in the catalog:

^{View the list of models}

Here, from the top bar, it is possible to:

^{Filters for model search}

• Search for Models by keywords
• Filter Models by status
  • Virtual ()
  • Created
• Filter by Privacy level
• Filter by tags
• Sort and change page
• Access the Model registration form

Furthermore, from each Model card, it is possible to access the following functions:

Download the Model to the local computer

Delete the Model

New Model Registration

To register a new Model, click on + Register Model from the Models management page; the registration form will open:

^{Model registration form}

At this point, the procedure is entirely analogous to what was seen during the Quickstart for uploading a Dataset (see Dataset Upload in Quickstart)

The only difference is that here you can also select the format of the Model to be uploaded.

The supported formats are the following:

format (Alida)	Framework / use	implementation in model-settings.json	parameters.uri (what it points to)	Main notes	Documentation
SKLearn	Serialized scikit-learn models (joblib/pickle)	mlserver_sklearn.SKLearnModel	Model file: e.g. ./model.joblib or ./model.pkl	Tabular sklearn models saved via joblib/pickle.	https://github.com/SeldonIO/MLServer/tree/master/runtimes/sklearn
XGBoost	XGBoost models	mlserver_xgboost.XGBoostModel	Model file: e.g. ./model.json or ./model.bst	Models saved with XGBoost API (save_model).	https://github.com/SeldonIO/MLServer/tree/master/runtimes/xgboost.html
LightGBM	LightGBM models	mlserver_lightgbm.LightGBMModel	Model file: e.g. ./model.txt / ./model.bst	Artifacts exported with Booster.save_model.	https://github.com/SeldonIO/MLServer/tree/master/runtimes/lightgbm.html
MLFlow	Models saved or registered with MLflow	mlserver_mlflow.MLflowRuntime	MLflow directory, e.g. ./model or models:/MyModel/1	Uses the MLflow folder (MLmodel, environment, etc.).	https://github.com/SeldonIO/MLServer/tree/master/runtimes/mlflow.html
HuggingFace	HuggingFace Transformers models/pipelines	mlserver_huggingface.HuggingFaceRuntime	Not always necessary; often configured via extra	For NLP/LLM; configuration via parameters.extra.	https://github.com/SeldonIO/MLServer/tree/master/runtimes/huggingface.html
Spark Mlib	Spark MLlib models	mlserver_mllib.MLlibModel	Path to pipeline saved from Spark MLlib	Loads pipelines exported via model.write().save.	https://github.com/SeldonIO/MLServer/tree/master/runtimes/mllib.html
Python	Custom models in Python	E.g. mlserver_python.PythonModel or your subclass	Directory with Python code + optional model file	Allows implementing load() and predict() manually.	https://github.com/SeldonIO/MLServer/tree/master/runtimes/python.html
MLServer	Catch-all for generic runtimes (Tempo, Alibi, custom)	Depends on the desired runtime	Directory with runtime artifacts (Tempo, Alibi, etc.)	Used for runtimes not covered above.	https://github.com/SeldonIO/MLServer/blob/master/docs/user-guide/custom.md