Merge remote-tracking branch 'origin/master'

Author: jlwalke2

examples/README.md

@@ -0,0 +1,124 @@
+
+Examples
+========
+
+- [Register a SAS classification model](#register-a-sas-classification-model)
+- [Register a SAS regression model](#register-a-sas-regression-model)
+- [Register a SAS deep learning model](#register-a-sas-deep-learning-model)
+
+- [Register a scikit-learn classification model](#register-a-scikit-learn-classification-model)
+- [Register a scikit-learn regression model](#register-a-scikit-learn-regression-model)
+
+- [Full model lifecycle](#full-model-lifecycle)
+- [Register a custom model](#register-a-custom-model)
+- [Register models with model metrics](#register-models-with-model-metrics)
+- [Modeling with Python & SAS AutoML](#modeling-with-python--sas-automl)
+- [Making direct REST API calls](#making-direct-rest-api-calls)
+
+---
+
+
+Register a SAS classification model
+------------------------------------
+Filename: [register_sas_classification_model.py](register_sas_classification_model.py)
+
+Level: Beginner
+
+Registers a classification model in SAS Model Manager that was created from a SAS algorithm with [SWAT](https://github.com/sassoftware/python-swat).
+
+
+
+Register a SAS regression model
+-------------------------------
+Filename: [register_sas_regression_model.py](register_sas_regression_model.py)
+
+Level: Beginner
+
+Registers a regression model in SAS Model Manager that was created from a SAS algorithm with [SWAT](https://github.com/sassoftware/python-swat).
+
+
+
+Register a SAS deep learning model
+----------------------------------
+Filename: [register_sas_dlpy_model.py](register_sas_dlpy_model.py)
+
+Level: Beginner
+
+Creates a SAS deep learning model using [dlpy](https://github.com/sassoftware/python-dlpy) and registers the model in SAS Model Manager.
+
+
+
+Register a scikit-learn classification model
+--------------------------------------------
+Filename: [register_scikit_classification_model.py](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](https://github.com/scikit-learn/scikit-learn).
+
+
+
+Register a scikit-learn regression model
+----------------------------------------
+Filename: [register_scikit_regression_model.py](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](https://github.com/scikit-learn/scikit-learn).
+
+
+
+Full model lifecycle
+--------------------
+Filename: [full_lifecycle.py](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](https://github.com/scikit-learn/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
+
+
+
+ Register a custom model
+ ------------------------
+ Filename: [register_custom_model.py](register_custom_model.py)
+
+ Level: Intermediate
+
+ Registers a model in SAS Model Manager by explicitly providing the files and model details.
+
+
+
+Register models with model metrics
+----------------------------------
+Filename: [FleetManagement.ipynb](FleetManagement.ipynb)
+
+Level: Intermediate
+
+Trains multiple tree-based models using [scikit-learn](https://github.com/scikit-learn/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.
+
+
+
+Modeling with Python & SAS AutoML
+-------------------------------
+Filename: [data_science_pilot.ipynb](data_science_pilot.ipynb)
+
+Level: Intermediate
+
+Uses the [swat](https://github.com/sassoftware/python-swat) package to perform automated modeling on a dataset.  Registers the results along with a custom XGBoost model to SAS Model Manager using `sasctl`.
+
+
+
+Making direct REST API calls
+--------------------------
+Filename: [direct_REST_calls.py](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.

examples/Viya 2020 Example.ipynb renamed to examples/data_science_pilot.ipynb

@@ -7340,9 +7340,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "py36_swatsasctl",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "py36_swatsasctl"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -7354,7 +7354,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,