notebooks: Quickstart for model documentation edit

notebooks/README.md

@@ -4,9 +4,10 @@ Our [Jupyter Notebook](https://jupyter.org/) code samples are designed to showca
 
 Sample notebooks are organized into the following folders:
 
-* `notebooks/code_samples` — Showcase end-to-end functionality for documenting models
-* `notebooks/how_to` — Learn how to use specific ValidMind features, e.g. how to list all test suites
+* `notebooks/quickstart` — Quick guides to get you started with ValidMind
 * `notebooks/tutorials` — Get step-by-step instructions and learn about ValidMind concepts in depth
+* `notebooks/how_to` — Learn how to use specific ValidMind features, for example how to list all test suites
+* `notebooks/code_samples` — Showcase end-to-end functionality for documenting or validating models
 * `notebooks/code_sharing` — Share your own notebooks or document code internally
 
 ## Getting started

notebooks/code_samples/custom_tests/implement_custom_tests.ipynb

@@ -426,7 +426,7 @@
    "source": [
     "Just so we can run all of the tests in the template, let's initialize the train and raw dataset.\n",
     "\n",
-    "(see the `quickstart_customer_churn_full_suite.ipynb` notebook and the ValidMind docs for more information on what we are doing here)"
+    "(Refer to [**Quickstart for model documentation**](../../quickstart/quickstart_model_documentation.ipynb) and the ValidMind docs for more information on what we are doing here)"
    ]
   },
   {

notebooks/templates/next-steps.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way: use the ValidMind Platform to work with your model documentation."
+    "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation."
    ]
   },
   {

notebooks/tutorials/model_development/2-start_development_process.ipynb

@@ -142,7 +142,7 @@
     "In our below example, note that: \n",
     "\n",
     "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
-    "- The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object."
+    "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
    ]
   },
   {
@@ -887,7 +887,7 @@
     "\n",
     "### Assign predictions\n",
     "\n",
-    "Once the model has been registered you can assign model predictions to the training and test datasets.\n",
+    "Once the model has been registered you can assign model predictions to the training and testing datasets.\n",
     "\n",
     "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n",
     "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n",
@@ -959,7 +959,7 @@
     "\n",
     "- [x] Import a sample dataset\n",
     "- [x] Identify which tests you might want to run with ValidMind\n",
-    "- [x] Initialize ValidMind datasets\n",
+    "- [x] Initialize ValidMind datasets and model objects\n",
     "- [x] Run individual tests\n",
     "- [x] Utilize the output from tests you've run\n",
     "- [x] Log test results from sets of or individual tests as evidence to the ValidMind Platform\n",

notebooks/tutorials/model_validation/2-start_validation_process.ipynb

@@ -147,7 +147,7 @@
     "In our below example, note that:\n",
     "\n",
     "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
-    "- The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object."
+    "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
    ]
   },
   {

scripts/run_e2e_notebooks.py

@@ -6,7 +6,7 @@
 Note: This script is meant to be run from the root of the repo
 
 Notebooks Tested:
- - notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb
+ - notebooks/quickstart/quickstart_model_documentation.ipynb
  - notebooks/code_samples/time_series/quickstart_time_series_full_suite.ipynb
  - notebooks/code_samples/regression/quickstart_regression_full_suite.ipynb
  - notebooks/code_samples/custom_tests/external_test_providers.ipynb
@@ -39,7 +39,7 @@
 )  # Demo Account Dev Customer Churn Model
 
 NOTEBOOKS_TO_RUN = [
-    "notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb",
+    "notebooks/quickstart/quickstart_model_documentation.ipynb",
     "notebooks/code_samples/time_series/quickstart_time_series_high_code.ipynb",
     "notebooks/code_samples/regression/quickstart_regression_full_suite.ipynb",
     "notebooks/how_to/run_unit_metrics.ipynb",
@@ -71,7 +71,7 @@
     },
     {
         # [Demo] Customer Churn Model
-        "path": "notebooks/code_samples/quickstart_customer_churn_full_suite.ipynb",
+        "path": "notebooks/quickstart/quickstart_model_documentation.ipynb",
         "model": "cm4lr52lw00a60jpbhmzh8cah",
     },
     {