validmind · validbeck · Dec 17, 2024 · Dec 13, 2024 · Dec 13, 2024 · Dec 13, 2024
diff --git a/site/_quarto.yml b/site/_quarto.yml
@@ -83,7 +83,7 @@ website:
          file: about/overview.qmd
        - text: "{{< fa rocket >}} Get Started"
          file: get-started/get-started.qmd
-        - text: "{{< fa circle-info >}} Guides"
+        - text: "{{< fa book >}} Guides"
          file: guide/guides.qmd
        - text: "{{< fa circle-question >}} FAQ"
          file: faq/faq.qmd

diff --git a/site/about/overview-model-documentation.qmd b/site/about/overview-model-documentation.qmd
@@ -27,7 +27,7 @@ It offers model developers a systematic approach to documenting and testing risk

 <!-- Using the variable in alt text messes up the image display  -->

-![The two main components of {{< var vm.product >}}. The {{< var validmind.developer >}} that integrates with your existing developer environment, and the {{< var validmind.platform >}}.](/about/deployment/validmind-architecture-overview.png){fig-alt="An image showing the two main components of ValidMind. The ValidMind Library that integrates with your existing developer environment, and the ValidMind Platform."}
+![The two main components of {{< var vm.product >}}: the {{< var validmind.developer >}} that integrates with your existing developer environment, and the {{< var validmind.platform >}}](/about/deployment/validmind-architecture-overview.png){fig-alt="An image showing the two main components of ValidMind: the ValidMind Library that integrates with your existing developer environment, and the ValidMind Platform"}

 The {{< var validmind.developer >}} consists of a client-side library, a {{< var vm.api >}} integration for models and testing, and validation tests that streamline the model development process. Implemented as a series of independent libraries in Python and R, our {{< var vm.developer >}} ensures compatibility and flexibility with diverse sets of developer environments and requirements.


diff --git a/site/developer/get-started-validmind-library.qmd b/site/developer/get-started-validmind-library.qmd
@@ -36,14 +36,15 @@ listing:
    - ../notebooks/code_samples/custom_tests/integrate_external_test_providers.ipynb
    - ../notebooks/code_samples/nlp_and_llm/prompt_validation_demo.ipynb
    - ../notebooks/code_samples/time_series/quickstart_time_series_full_suite.ipynb
-  - id: developer-workflow
+  - id: document-models
    type: grid
    grid-columns: 2
    max-description-length: 250
    sort: false
    fields: [title, description]
-    contents:
+    contents: 
    - model-documentation/documenting-models.qmd
+    - ../guide/model-documentation/working-with-model-documentation.qmd
 ---

 The {{< var validmind.developer >}} helps you streamline model documentation by automating the generation of drafts. All you need to do is upload your documentation artifacts and test results to the {{< var validmind.platform >}}.
@@ -54,7 +55,7 @@ The {{< var validmind.developer >}} provides a rich collection of documentation

 <!-- Using the variable in alt text messes up the image display  --> 

-![](/get-started/validmind-lifecycle.jpg){width=70% fig-alt="An image showing the two main components of ValidMind. The ValidMind Library that integrates with your existing developer environment, and the ValidMind Platform."}
+![The two main components of {{< var vm.product >}}: the {{< var validmind.developer >}} that integrates with your existing developer environment, and the {{< var validmind.platform >}}](/get-started/validmind-lifecycle.jpg){width=70% fig-alt="An image showing the two main components of ValidMind: the ValidMind Library that integrates with your existing developer environment, and the ValidMind Platform"}

 {{< var vm.product >}} offers two primary methods for automating model documentation:

@@ -89,32 +90,57 @@ After you [**sign up**]({{< var url.us1 >}}) for {{< var vm.product >}} to get a

 ## Learn how to run tests

+:::: {.flex .flex-wrap .justify-around}
+
+::: {.w-80-ns}
 The {{< var validmind.developer >}} provides many built-in tests and test suites which make it easy for developers to automate their model documentation. Start by running a pre-made test, then modify it, and finally create your own test: 

-:::{#developer-how-to-beginner}
 :::

-{{< fa hand-point-right >}} Learn more: [Run tests & test suites](model-testing/testing-overview.qmd)
+::: {.w-20-ns .tc}
+[Run tests & test suites](model-testing/testing-overview.qmd){.button .button-green}
+
+:::
+
+::::
+
+:::{#developer-how-to-beginner}
+:::

 ## Try the code samples

+:::: {.flex .flex-wrap .justify-around}
+
+::: {.w-80-ns}
 Our code samples showcase the capabilities of the {{< var validmind.developer >}}. Examples that you can build on and adapt for your own use cases include:

+:::
+
+::: {.w-20-ns .tc}
+[Code samples](samples-jupyter-notebooks.qmd){.button .button-green}
+
+:::
+
+::::
+
 :::{#developer-code-samples}
 ::: 

-{{< fa hand-point-right >}} Try more: [Code samples](samples-jupyter-notebooks.qmd)
+## Document models

-## What's next
+:::: {.flex .flex-wrap .justify-around}

-After you have tried out the {{< var validmind.developer >}}, continue [working with your model documentation](/guide/model-documentation/working-with-model-documentation.qmd) in the {{< var validmind.platform >}} online. There, you can:
+::: {.w-80-ns}
+After you have tried out the {{< var validmind.developer >}}, continue working with your model documentation in the {{< var validmind.platform >}}:

- Work with documentation templates to customize them to your specific needs
- Work with model documentation in the {{< var vm.platform >}} to make edits, collaborate with validators, and submit your model documentation for approval
- Export your finalized model documentation
+:::

-For more in-depth guides, check out our breakdown of your complete journey as a model developer with {{< var vm.product >}}: 
+::: {.w-20-ns .tc}
+[Supported models](model-documentation/supported-models.qmd){.button .button-green}

-:::{#developer-workflow}
 :::

+::::
+
+:::{#document-models}
+:::
diff --git a/site/developer/model-documentation/install-and-initialize-validmind-library.qmd b/site/developer/model-documentation/install-and-initialize-validmind-library.qmd
@@ -102,11 +102,11 @@ Version: 2.5.15
    %pip install --upgrade validmind
    ```

-::: {.column-margin}
+<!-- ::: {.column-margin}
 ::: {.callout title="Current version:"}
 {{< var version.validmind >}}
 :::
-:::
+::: -->


 <!-- FOOTNOTES -->

diff --git a/site/developer/model-documentation/supported-models.qmd b/site/developer/model-documentation/supported-models.qmd
@@ -3,6 +3,16 @@ title: "Supported models"
 date: last-modified
 aliases:
  - ../../guide/supported-models.html
+listing:
+  - id: next-models
+    type: grid
+    max-description-length: 250
+    sort: false
+    fields: [title, description]
+    contents:
+    - ../model-testing/testing-overview.qmd
+    - ../model-testing/test-descriptions.qmd
+    - ../samples-jupyter-notebooks.qmd
 ---

 The {{< var validmind.developer >}} provides out-of-the-box support for testing and documentation for an array of model types and modeling packages.
@@ -11,80 +21,134 @@ The {{< var validmind.developer >}} provides out-of-the-box support for testing

 A _supported model_ refers to a model for which predefined testing or documentation functions exist in the {{< var validmind.developer >}}, provided that the model you are developing is documented using a supported version of our {{< var vm.developer >}}. These model types cover a very large portion of the models used in commercial and retail banking. 

-Given the rapid developments in the AI space, including the advent of large language models (LLMs), {{< var vm.product >}} product development has also focused on making sure that our {{< var vm.developer >}} is extensible to support future model types or modeling packages, so that we do not limit our users to specific model types. You always have the flexibility to:
-
- [Implement custom tests](/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb)
- [Integrate external test providers](/notebooks/code_samples/custom_tests/integrate_external_test_providers.ipynb)
+::: {.callout}
+## {{< var vm.product >}} does not limit our users to specific model types.
+
+- The {{< var validmind.developer >}} is extensible to support future model types or modeling packages to accomodate rapid developments in the AI space, including the advent of large language models (LLMs).
+- You always have the flexibility to implement custom tests and integrate external test providers.[^1]
+:::

 <!--- Please note the inherent dependency on data types, such as tabular, time series, and text data types. This distinction isn't always immediately evident. For example: while binary classification is possible on text datasets, only the `tabular_dataset` suite is explicitly mentioned in our list of supported models.--->

 ## Supported model types

+::: {.column-margin}
+::: {.feature}
+Vendor models
+: {{< var vm.product >}} offers support for both first-party models and [third-party vendor models](/about/glossary/glossary.qmd#vendor-model).
+
+:::
+
+:::
+
 ### Traditional statistical models

- Linear regression — Models relationship between a scalar response and one or more explanatory variables.
- Logistic regression — Predicts the probability of a binary outcome based on one or more predictor variables.
- Time series — Analyzes data points collected or sequenced over time.
+:::: {.flex .flex-wrap .justify-around}
+
+::: {.w-30-ns}
+
+#### Linear regression
+Models relationship between a scalar response and one or more explanatory variables.
+
+:::
+
+::: {.w-30-ns}
+
+#### Logistic regression
+Models relationship between a scalar response and one or more explanatory variables.
+
+:::
+
+::: {.w-30-ns}
+#### Time series 
+Analyzes data points collected or sequenced over time.
+
+:::
+
+::::

 ### Machine learning models

-Hugging Face-compatible models
-: - Natural language processing (NLP) text classification — Categorizes text into predefined classes.
+:::: {.flex .flex-wrap .justify-around}
+
+::: {.w-50-ns .pr2}
+#### Hugging Face-compatible models
+- Natural language processing (NLP) text classification — Categorizes text into predefined classes.
 - Tabular classification — Assigns categories to tabular dataset entries.
 - Tabular regression — Predicts continuous outcomes from tabular data.

-Tree-based models (XGBoost / CatBoost / random forest)
-: - Classification — Predicts categorical outcomes using decision trees.
+#### Neural networks
+- Long short-term memory (LSTM) — Processes sequences of data, remembering inputs over long periods.
+- Recurrent neural network (RNN) — Processes sequences by maintaining a state that reflects the history of processed elements.
+- Convolutional neural network (CNN) — Primarily used for processing grid-like data such as images.
+
+
+:::
+
+::: {.w-50-ns .pl2 .pr2}
+
+#### Tree-based models <br> (XGBoost / CatBoost / random forest)
+- Classification — Predicts categorical outcomes using decision trees.
 - Regression — Predicts continuous outcomes using decision trees.

-K-nearest neighbors (KNN)
-: - Classification — Assigns class by majority vote of the k-nearest neighbors.
+#### K-nearest neighbors (KNN)
+- Classification — Assigns class by majority vote of the k-nearest neighbors.
 - Regression — Predicts value based on the average of the k-nearest neighbors.

-Clustering
-: - K-means — Partitions _n_ observations into _k_ clusters in which each observation belongs to the cluster with the nearest mean.
+#### Clustering
+- K-means — Partitions _n_ observations into _k_ clusters in which each observation belongs to the cluster with the nearest mean.

-Neural networks
-: - Long short-term memory (LSTM) — Processes sequences of data, remembering inputs over long periods.
- Recurrent neural network (RNN) — Processes sequences by maintaining a state that reflects the history of processed elements.
- Convolutional neural network (CNN) — Primarily used for processing grid-like data such as images.
+
+:::
+
+::::

 ### Generative AI models

-Large language models (LLMs)
-: - Classification — Categorizes input into predefined classes.
+#### Large language models (LLMs)
+- Classification — Categorizes input into predefined classes.
 - Text summarization — Generates concise summaries from longer texts.

-:::{.callout}
-{{< var vm.product >}} offers support for both first-party models and [third-party vendor models](/about/glossary/glossary.qmd#vendor-model).
-:::
-
 ## Supported modeling libraries and other tools

 :::: {.flex .flex-wrap .justify-around}

 ::: {.w-50-ns}

- [scikit-learn](https://scikit-learn.org/stable/) — A Python library for machine learning, offering a range of supervised and unsupervised learning algorithms.
- [statsmodels](https://www.statsmodels.org/stable/index.html) — A Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and exploring data.
- [PyTorch](https://pytorch.org/) — An open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
- [Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index) — Provides thousands of pre-trained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, and text generation.
- [XGBoost](https://xgboost.readthedocs.io/en/stable/) — An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable, implementing machine learning algorithms under the Gradient Boosting framework.
+- **[scikit-learn](https://scikit-learn.org/stable/)** — A Python library for machine learning, offering a range of supervised and unsupervised learning algorithms.
+
+- **[statsmodels](https://www.statsmodels.org/stable/index.html)** — A Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and exploring data.
+
+- **[PyTorch](https://pytorch.org/)** — An open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
+
+- **[Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index)** — Provides thousands of pre-trained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, and text generation.
+
+- **[XGBoost](https://xgboost.readthedocs.io/en/stable/)** — An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable, implementing machine learning algorithms under the Gradient Boosting framework.

 :::

 ::: {.w-50-ns}

- [CatBoost](https://catboost.ai/) — An open-source gradient boosting on decision trees library with categorical feature support out of the box, for ranking, classification, regression, and other ML tasks.
- [LightGBM](https://lightgbm.readthedocs.io/en/stable/) — A fast, distributed, high-performance gradient boosting (GBDT, GBRT, GBM, or MART) framework based on decision tree algorithms, used for ranking, classification, and many other machine learning tasks.
- R models, via [rpy2 - R in Python](https://rpy2.github.io/) — Facilitates the integration of R's statistical computing and graphics capabilities with Python, allowing for R models to be called from Python.
- Large language models (LLMs), via [OpenAI-compatible APIs](https://platform.openai.com/docs/introduction) — Access to advanced AI models trained by OpenAI for a variety of natural language tasks, including text generation, translation, and analysis, through a compatible API interface. This support includes both the OpenAI API and the Azure OpenAI Service via API.
+- **[CatBoost](https://catboost.ai/)** — An open-source gradient boosting on decision trees library with categorical feature support out of the box, for ranking, classification, regression, and other ML tasks.
+
+- **[LightGBM](https://lightgbm.readthedocs.io/en/stable/)** — A fast, distributed, high-performance gradient boosting (GBDT, GBRT, GBM, or MART) framework based on decision tree algorithms, used for ranking, classification, and many other machine learning tasks.
+
+- **R models, via [rpy2 - R in Python](https://rpy2.github.io/)** — Facilitates the integration of R's statistical computing and graphics capabilities with Python, allowing for R models to be called from Python.
+
+- **Large language models (LLMs), via [OpenAI-compatible APIs](https://platform.openai.com/docs/introduction)** — Access to advanced AI models trained by OpenAI for a variety of natural language tasks, including text generation, translation, and analysis, through a compatible API interface. This support includes both the OpenAI API and the Azure OpenAI Service via API.
 :::

 ::::

 ## What's next

- [Run tests & test suites](/developer/model-testing/testing-overview.qmd)
- [Test descriptions](/developer/model-testing/test-descriptions.qmd)
- [Code samples](/developer/samples-jupyter-notebooks.qmd)
+:::{#next-models}
+:::
+
+
+<!-- FOOTNOTES -->
+
+[^1]: 
+
+  - [Implement custom tests](/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb)
+  - [Integrate external test providers](/notebooks/code_samples/custom_tests/integrate_external_test_providers.ipynb)
diff --git a/site/developer/model-testing/test-descriptions.qmd b/site/developer/model-testing/test-descriptions.qmd
@@ -30,10 +30,10 @@ listing:
    fields: [title, description]  
 ---

-This topic describes the tests that are available as part of the {{< var validmind.developer >}}, grouped by type of validation or monitoring test.
+Tests that are available as part of the {{< var validmind.developer >}}, grouped by type of validation or monitoring test.

 ::: {.callout}
-## {{< fa flask >}} [Try the test sandbox (BETA)](test-sandbox.qmd)
+## {{< fa flask >}} [Try the test sandbox <sup>[beta]{.smallcaps}</sup>](test-sandbox.qmd)

 Explore our interactive sandbox to see what tests are available in the {{< var validmind.developer >}}.
 :::

diff --git a/site/developer/model-testing/test-sandbox.qmd b/site/developer/model-testing/test-sandbox.qmd
@@ -1,5 +1,5 @@
 ---
-title: "Test sandbox (BETA)"
+title: "Test sandbox <sup>[beta]{.smallcaps}</sup>"
 date: last-modified
 aliases:
  - ../../guide/test-sandbox.html

diff --git a/site/developer/model-testing/testing-overview.qmd b/site/developer/model-testing/testing-overview.qmd
@@ -145,6 +145,6 @@ Absolutely! {{< var vm.product >}} supports custom tests that you develop yourse
 :::{#tests-custom}
 :::

-## Reference
+## {{< var validmind.api >}} reference

-<a href="https://docs.validmind.ai/validmind/validmind.html" target="_blank">{{< var validmind.developer >}} Reference</a> 
+[{{< var validmind.developer >}} Reference](https://docs.validmind.ai/validmind/validmind.html){target="_blank" .button .button-green}