[HWORKS-1113] Add docs for attaching model evaluation images to a mod…

…el (#362)

docs/user_guides/mlops/registry/input_example.md

@@ -1,3 +1,7 @@
+---
+description: Documentation on how to attach an input example to a model.
+---
+
 # How To Attach An Input Example
 
 ## Introduction

docs/user_guides/mlops/registry/model_evaluation_images.md

@@ -0,0 +1,79 @@
+---
+description: Documentation on how to attach model evaluation images to a model.
+---
+
+# How To Save Model Evaluation Images
+
+## Introduction
+
+In this guide, you will learn how to attach ==model evaluation images== to a model. Model evaluation images are images that visually describe model performance metrics. For example, **confusion matrices**, **ROC curves**, **model bias tests**, and **training loss curves** are examples of common model evaluation images. By attaching model evaluation images to your versioned model, other users can better understand the model performance and evaluation metrics.
+
+## Code
+
+### Step 1: Connect to Hopsworks
+
+```python
+import hopsworks
+
+project = hopsworks.login()
+
+# get Hopsworks Model Registry handle
+mr = project.get_model_registry()
+```
+
+### Step 2: Generate model evaluation images
+
+Generate an image that visualizes model performance and evaluation metrics
+
+```python
+import seaborn
+from sklearn.metrics import confusion_matrix
+
+# Predict the training data using the trained model
+y_pred_train = model.predict(X_train)
+
+# Predict the test data using the trained model
+y_pred_test = model.predict(X_test)
+
+# Calculate and print the confusion matrix for the test predictions
+results = confusion_matrix(y_test, y_pred_test)
+
+# Create a DataFrame for the confusion matrix results
+df_confusion_matrix = pd.DataFrame(
+    results, 
+    ['True Normal', 'True Fraud'],
+    ['Pred Normal', 'Pred Fraud'],
+)
+
+# Create a heatmap using seaborn with annotations
+heatmap = seaborn.heatmap(df_confusion_matrix, annot=True)
+
+# Get the figure and display it
+fig = heatmap.get_figure()
+fig.show()
+```
+
+### Step 3: Save the figure to a file inside the model directory
+
+Save the figure to a file with a common filename extension (for example, .png or .jpeg), and place it in a directory called `images` - a subdirectory of the model directory that is registered to Hopsworks.
+
+```python
+# Specify the directory name for saving the model and related artifacts
+model_dir = "./model"
+
+# Create a subdirectory of model_dir called 'images' for saving the model evaluation images
+model_images_dir = model_dir + "/images"
+if not os.path.exists(model_images_dir):
+    os.mkdir(model_images_dir)
+
+# Save the figure to an image file in the images directory
+fig.savefig(model_images_dir + "/confusion_matrix.png")
+
+# Register the model
+py_model = mr.python.create_model(name="py_model")
+py_model.save("./model")
+```
+
+## Conclusion
+
+In this guide you learned how to attach model evaluation images to a model, visually communicating the model performance and evaluation metrics in the model registry.

docs/user_guides/mlops/registry/model_schema.md

@@ -1,3 +1,7 @@
+---
+description: Documentation on how to attach a model schema to a model.
+---
+
 # How To Attach A Model Schema
 
 ## Introduction

mkdocs.yml

@@ -182,6 +182,7 @@ nav:
                   - Python: user_guides/mlops/registry/frameworks/python.md
               - Model Schema: user_guides/mlops/registry/model_schema.md
               - Input Example: user_guides/mlops/registry/input_example.md
+              - Model Evaluation Images: user_guides/mlops/registry/model_evaluation_images.md
           - Model Serving:
               - user_guides/mlops/serving/index.md
               - Deployment: