diff --git a/shiny/api-examples/layout_columns/app.py b/shiny/api-examples/layout_columns/app.py
new file mode 100644
index 000000000..4691f902c
--- /dev/null
+++ b/shiny/api-examples/layout_columns/app.py
@@ -0,0 +1,33 @@
+from model_plots import *  # model plots and cards
+
+from shiny import App, Inputs, Outputs, Session, render, ui
+
+app_ui = ui.page_fluid(
+    ui.panel_title(ui.h2("Model Dashboard")),
+    ui.markdown("Using `ui.layout_columns()` for the layout."),
+    ui.layout_columns(
+        card_loss,
+        card_acc,
+        card_feat,
+        col_widths={"sm": (5, 7, 12)},
+        # row_heights=(2, 3),
+        # height="700px",
+    ),
+)
+
+
+def server(input: Inputs, output: Outputs, session: Session):
+    @render.plot
+    def loss_over_time():
+        return plot_loss_over_time()
+
+    @render.plot
+    def accuracy_over_time():
+        return plot_accuracy_over_time()
+
+    @render.plot
+    def feature_importance():
+        return plot_feature_importance()
+
+
+app = App(app_ui, server)
diff --git a/shiny/api-examples/layout_columns/model_plots.py b/shiny/api-examples/layout_columns/model_plots.py
new file mode 100644
index 000000000..951204df9
--- /dev/null
+++ b/shiny/api-examples/layout_columns/model_plots.py
@@ -0,0 +1,56 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from shiny import ui
+
+
+def plot_loss_over_time():
+    epochs = np.arange(1, 101)
+    loss = 1000 / np.sqrt(epochs) + np.random.rand(100) * 25
+
+    fig = plt.figure(figsize=(10, 6))
+    plt.plot(epochs, loss)
+    plt.xlabel("Epochs")
+    plt.ylabel("Loss")
+    return fig
+
+
+def plot_accuracy_over_time():
+    epochs = np.arange(1, 101)
+    accuracy = np.sqrt(epochs) / 12 + np.random.rand(100) * 0.15
+    accuracy = [np.min([np.max(accuracy[:i]), 1]) for i in range(1, 101)]
+
+    fig = plt.figure(figsize=(10, 6))
+    plt.plot(epochs, accuracy)
+    plt.xlabel("Epochs")
+    plt.ylabel("Accuracy")
+    return fig
+
+
+def plot_feature_importance():
+    features = ["Product Category", "Price", "Brand", "Rating", "Number of Reviews"]
+    importance = np.random.rand(5)
+
+    fig = plt.figure(figsize=(10, 6))
+    plt.barh(features, importance)
+    plt.xlabel("Importance")
+    return fig
+
+
+card_loss = ui.card(
+    ui.card_header("Loss Over Time"),
+    ui.output_plot("loss_over_time"),
+    full_screen=True,
+)
+
+card_acc = ui.card(
+    ui.card_header("Accuracy Over Time"),
+    ui.output_plot("accuracy_over_time"),
+    full_screen=True,
+)
+
+card_feat = ui.card(
+    ui.card_header("Feature Importance"),
+    ui.output_plot("feature_importance"),
+    full_screen=True,
+)