diff --git a/.github/workflows/docs-deploy.yml b/.github/workflows/docs-deploy.yml
index fe267291a..f854ed70d 100644
--- a/.github/workflows/docs-deploy.yml
+++ b/.github/workflows/docs-deploy.yml
@@ -98,6 +98,8 @@ jobs:
           server: ${{ secrets.DOCS_SERVER }}
           username: ${{ secrets.DOCS_USERNAME }}
           password: ${{ secrets.DOCS_PASSWORD }}
+          log-level: verbose
+          timeout: 60000
           local-dir: docs/build/html/
           server-dir: ./  # deploy to the root dir
           exclude: |  # don't delete the /ver/ dir
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 66b3c9317..76298d4ff 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -88,8 +88,6 @@
 templates_path = ["_templates"]
 exclude_patterns = []
 
-napoleon_custom_sections = ["Features"]
-
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
diff --git a/examples/machine_learning/kmeans.py b/examples/machine_learning/kmeans.py
new file mode 100644
index 000000000..620fa15fb
--- /dev/null
+++ b/examples/machine_learning/kmeans.py
@@ -0,0 +1,119 @@
+"""
+K-Means Clustering of MNIST Dataset
+===================================
+
+Example showing how you can perform K-Means clustering on the MNIST dataset.
+"""
+
+# test_example = false
+# sphinx_gallery_pygfx_docs = 'screenshot'
+
+import fastplotlib as fpl
+import numpy as np
+from sklearn.datasets import load_digits
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+
+# load the data
+mnist = load_digits()
+
+# get the data and labels
+data = mnist['data'] # (1797, 64)
+labels = mnist['target'] # (1797,)
+
+# visualize the first 5 digits
+# NOTE: this is just to give a sense of the dataset if you are unfamiliar,
+# the more interesting visualization is below :D
+fig_data = fpl.Figure(shape=(1, 5), size=(900, 300))
+
+# iterate through each subplot
+for i, subplot in enumerate(fig_data):
+    # reshape each image to (8, 8)
+    subplot.add_image(data[i].reshape(8,8), cmap="gray", interpolation="linear")
+    # add the label as a title
+    subplot.set_title(f"Label: {labels[i]}")
+    # turn off the axes and toolbar
+    subplot.axes.visible = False
+    subplot.toolbar  = False
+
+fig_data.show()
+
+# project the data from 64 dimensions down to the number of unique digits
+n_digits = len(np.unique(labels)) # 10
+
+reduced_data = PCA(n_components=n_digits).fit_transform(data) # (1797, 10)
+
+# performs K-Means clustering, take the best of 4 runs
+kmeans = KMeans(n_clusters=n_digits, n_init=4)
+# fit the lower-dimension data
+kmeans.fit(reduced_data)
+
+# get the centroids (center of the clusters)
+centroids = kmeans.cluster_centers_
+
+# plot the kmeans result and corresponding original image
+figure = fpl.Figure(
+    shape=(1,2),
+    size=(700, 400),
+    cameras=["3d", "2d"],
+    controller_types=[["fly", "panzoom"]]
+)
+
+# set the axes to False
+figure[0, 0].axes.visible = False
+figure[0, 1].axes.visible = False
+
+figure[0, 0].set_title(f"K-means clustering of PCA-reduced data")
+
+# plot the centroids
+figure[0, 0].add_scatter(
+    data=np.vstack([centroids[:, 0], centroids[:, 1], centroids[:, 2]]).T,
+    colors="white",
+    sizes=15
+)
+# plot the down-projected data
+digit_scatter = figure[0,0].add_scatter(
+    data=np.vstack([reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2]]).T,
+    sizes=5,
+    cmap="tab10", # use a qualitative cmap
+    cmap_transform=kmeans.labels_, # color by the predicted cluster
+)
+
+# initial index
+ix = 0
+
+# plot the initial image
+digit_img = figure[0, 1].add_image(
+    data=data[ix].reshape(8,8),
+    cmap="gray",
+    name="digit",
+    interpolation="linear"
+)
+
+# change the color and size of the initial selected data point
+digit_scatter.colors[ix] = "magenta"
+digit_scatter.sizes[ix] = 10
+
+# define event handler to update the selected data point
+@digit_scatter.add_event_handler("pointer_enter")
+def update(ev):
+    # reset colors and sizes
+    digit_scatter.cmap = "tab10"
+    digit_scatter.sizes = 5
+
+    # update with new seleciton
+    ix = ev.pick_info["vertex_index"]
+
+    digit_scatter.colors[ix] = "magenta"
+    digit_scatter.sizes[ix] = 10
+
+    # update digit fig
+    figure[0, 1]["digit"].data = data[ix].reshape(8, 8)
+
+figure.show()
+
+# NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
+# please see our docs for using fastplotlib interactively in ipython and jupyter
+if __name__ == "__main__":
+    print(__doc__)
+    fpl.loop.run()
\ No newline at end of file