The /examples directory includes the examples listed below. Additionally, the /data
directory includes the model file outputs of the examples as well as the data sets
utilized by the examples.
Older examples are kept in the /ARCHIVE directory and sorted by their version of
python-sasctl.
Filename: pzmm_binary_classification_model_import.ipynb
Level: Beginner
Registers a trio of classification models in SAS Model Manager that were created from a Python algorithm with scikit-learn.
Filename: pzmm_regression_model_import.ipynb
Level: Beginner
Registers a regression model in SAS Model Manager that was created from a Python algorithm with scikit-learn.
Filename: pzmm_multi_classification_model_import.ipynb
Level: Beginner
Registers a multiclass classification model in SAS Model Manager that was created from a Python algorithm with scikit-learn.
Filename: pzmm_mlflow_model_import.ipynb
Level: Intermediate
Registers a classification model in SAS Model Manager that was created from a Python algorithm with MLflow.
Filename: pzmm_h2o_model_import.ipynb
Level: Intermediate
Registers a classification model in SAS Model Manager that was created from a Python algorithm with H2O.ai.
Filename: pzmm_generate_requirements_json.ipynb
Level: Intermediate
Generates a requirements.json file which includes the minimal number of dependencies required to run a Python model
Filename: pzmm_custom_kpis.ipynb
Level: Intermediate
Create and update custom model parameters and kpis on SAS Model Manager.
Filename: register_sas_classification_model.py
Level: Beginner
Registers a classification model in SAS Model Manager that was created from a SAS algorithm with SWAT.
Filename: register_sas_regression_model.py
Level: Beginner
Registers a regression model in SAS Model Manager that was created from a SAS algorithm with SWAT.
Filename: register_sas_dlpy_model.py
Level: Beginner
Creates a SAS deep learning model using dlpy and registers the model in SAS Model Manager. (WARNING: Does not work with Python 3.10 and later)
Filename: register_scikit_classification_model.py
Level: Beginner
Registers a classification model in SAS Model Manager that was created from a Python algorithm with scikit-learn.
Filename: register_scikit_regression_model.py
Level: Beginner
Registers a regression model in SAS Model Manager that was created from a Python algorithm with scikit-learn.
Filename: full_lifecycle.py
Level: Beginner
Demonstrates how sasctl can be used throughout the lifecycle of a model by:
- training multiple Python models with scikit-learn
- registering them to SAS Model Manager
- publishing them to SAS's real-time scoring engine (MAS)
- executing the models in real-time
- creating a report to track model performance over time
Filename: register_custom_model.py
Level: Intermediate
Registers a model in SAS Model Manager by explicitly providing the files and model details.
Filename: FleetManagement.ipynb
Level: Intermediate
Trains multiple tree-based models using scikit-learn and registers them in SAS Model Manager. Also uses the pzmm module of sasctl to generate and include model fit statistics and ROC/Lift charts.
Filename: data_science_pilot.ipynb
Level: Intermediate
Uses the swat package to perform automated modeling on a dataset. Registers the results along with a custom XGBoost model to SAS Model Manager using sasctl.
Filename: direct_REST_calls.py
Level: Advanced
Demonstrates using sasctl to make REST calls over HTTP(S) directly to the SAS microservices.
Use if you need to customize behavior or use functionality not yet exposed through higher-level sasctl functions.
Filename: register_Azure_OpenAI_model_using_REST_calls.ipynb
Level: Intermediate
Leverages a GPT-3.5-Turbo model from Azure OpenAI in SAS® Model Manager and SAS® Intelligent Decisioning.