Merge pull request #3753 from vespa-engine/kkraune-patch-1

fix link, spellcheck

Author: kkraune

en/getting-started.html

@@ -25,7 +25,7 @@
       <li>Docker Desktop: <a href="vespa-quick-start.html">Install and run Vespa locally</a></li>
       <li>Docker Desktop: <a href="vespa-quick-start-java.html">Install and run Vespa locally, with Java components</a></li>
     </ul>
-    <p>The <a href="/en/developer-guide.html">developer guide</a> is an intro to developing, testing and deploying applications.</p>
+    <p>The <a href="/en/developer-guide.html">developer guide</a> is an intro to developing, testing, and deploying applications.</p>
     <p>Until you add multiple nodes an application can be deployed both on cloud and locally with no modifications.</p>
   </td>
 </tr>
@@ -42,7 +42,7 @@
     </li>
 
     <li><a href="tutorials/hybrid-search.html">Tutorial: Hybrid Text Search</a>.
-      A search tutorial and introduction to hybrid text ranking with Vespa combining BM25 with text embedding models.
+      A search tutorial and introduction to hybrid text ranking with Vespa, combining BM25 with text embedding models.
       </li>
 
     <li><a href="tutorials/text-search-ml.html">Tutorial: Improving Text Search with Machine Learning</a>.
@@ -74,16 +74,16 @@
   <strong>ML Model Serving</strong>
   <p>
     Learn how to use Vespa for ML model serving in <a href="stateless-model-evaluation.html">Stateless Model Evaluation</a>.
-    Vespa has support for running inference with models from many popular ML frameworks which can be used
-    for ranking, query classification, question answering, multi-modal retrieval and more.
+    Vespa supports running inference with models from many popular ML frameworks, which can be used
+    for ranking, query classification, question answering, multi-modal retrieval, and more.
   </p>
     <ul>
       <li><a href="onnx.html">Ranking with ONNX models</a>. Export models from
        popular deep learning frameworks such as <a href="https://pytorch.org/docs/stable/onnx.html">PyTorch</a>
       to <a href="https://onnx.ai/">ONNX</a> format for serving in Vespa. Vespa integrates with
       <a href="https://blog.vespa.ai/stateful-model-serving-how-we-accelerate-inference-using-onnx-runtime/">ONNX-Runtime</a>
-      for <a href="https://blog.vespa.ai/stateless-model-evaluation/">accelerated inference</a>. Many ML framework
-      support exporting model to ONNX, including <a href="http://onnx.ai/sklearn-onnx/">sklearn</a>.
+      for <a href="https://blog.vespa.ai/stateless-model-evaluation/">accelerated inference</a>. Many ML frameworks
+      support exporting models to ONNX, including <a href="http://onnx.ai/sklearn-onnx/">sklearn</a>.
     </li>
       <li><a href="lightgbm.html">Ranking with LightGBM models</a></li>
       <li><a href="xgboost.html">Ranking with XGBoost models</a></li>
@@ -92,7 +92,7 @@
 
   <strong>Embedding Model Inference</strong>
   <p>
-   Vespa supports integrating <a href="embedding.html">embedding</a> models, this avoids transferring large amounts of embedding vector data
+   Vespa supports integrating <a href="embedding.html">embedding</a> models, which avoids transferring large amounts of embedding vector data
    over the network and allows for efficient serving of embedding models.
   <ul>
     <li><a href="embedding.html#huggingface-embedder">Huggingface Embedder</a> Use single-vector embedding models from Hugging face</li>
@@ -110,12 +110,12 @@
 
   <strong>E-Commerce Search</strong>
   <p>The <a href="use-case-shopping.html">e-commerce shopping sample application</a> demonstrates Vespa grouping,
-  true in-place partial updates, custom ranking and more.</p>
+  true in-place partial updates, custom ranking, and more.</p>
 
   <strong>Examples and starting sample applications</strong>
 <p>
 There are many examples and starting applications on
-<a href="https://github.com/vespa-engine/sample-apps/">GitHub</a> and <a href="https://pyvespa.readthedocs.io/en/latest/examples.html">PyVespa examples</a>.
+<a href="https://github.com/vespa-engine/sample-apps/">GitHub</a> and <a href="https://vespa-engine.github.io/pyvespa/index.html">Pyvespa examples</a>.
 </p>
   </td>
 </tr>
@@ -144,9 +144,9 @@
   <th class="p-t-10">Custom component development</th>
   <td class="p-t-10">
     <p>
-      Vespa applications can contain custom components to be run by Vespa, for example on receiving queries or documents.
+      Vespa applications can contain custom components that are run by Vespa, for example, when receiving queries or documents.
       The applications must be able to run on a JVM.
-      While all the built-in behavior of Vespa can be invoked by an YQL query,
+      While all the built-in behavior of Vespa can be invoked by a YQL query,
       advanced applications often choose to use plugin components to build queries from frontend requests
       as doing this closer to the data is faster and simpler.
     </p>