another small revision

docs/source/examples/best-practices.rst

@@ -44,14 +44,14 @@ We also recommend allocating most, though not all, of the GPU memory space. We d
 
 Additionally, when using `Accelerated Networking`_ , we only need to register a single IPC handle for the whole pool (which is expensive, but only done once) since from the IPC point of viewer there's only a single allocation. As opposed to just using RMM without a pool where each new allocation must be registered with IPC.
 
-Spilling from device
+Spilling from Device
 ~~~~~~~~~~~~~~~~~~~~
 
 Dask-CUDA offers several different ways to enable automatic spilling from device memory.
 The best method often depends on the specific workflow. For classic ETL workloads using
 `Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_, native cuDF spilling is usually
 the best place to start. See :ref:`Dask-CUDA's spilling documentation <spilling-from-device>`
-page for more details.
+for more details.
 
 Accelerated Networking
 ~~~~~~~~~~~~~~~~~~~~~~