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examples/simple/data/data.ipynb

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    "source": [
     "# Simple example: data engineering\n",
     "\n",
-    "Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus) by taking full advantage of `great-ai`. Subsequently, we create a production-ready deployment.\n",
+    "Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus) by taking full advantage of `great-ai`. Subsequently, we create a production-ready deployment.\n",
     "\n",
     "![position of this step in the lifecycle](/media/scope-data.svg)\n",
     "> The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook.\n",

examples/simple/data/index.html

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@@ -1988,7 +1988,7 @@ Licensed under the Apache License, Version 2.0.
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h1 id="simple-example-data-engineering">Simple example: data engineering<a class="anchor-link" href="#simple-example-data-engineering">&#182;</a></h1><p>Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the <a href="https://api.semanticscholar.org/corpus">semantic scholar dataset</a> by taking full advantage of <code>great-ai</code>. Subsequently, we create a production-ready deployment.</p>
+<h1 id="simple-example-data-engineering">Simple example: data engineering<a class="anchor-link" href="#simple-example-data-engineering">&#182;</a></h1><p>Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the <a href="https://api.semanticscholar.org/corpus">semantic scholar dataset</a> by taking full advantage of <code>great-ai</code>. Subsequently, we create a production-ready deployment.</p>
 <p><img src="/media/scope-data.svg" alt="position of this step in the lifecycle"></p>
 <blockquote><p>The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook.</p>
 </blockquote>
@@ -2416,7 +2416,7 @@ add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2)</div>
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       Last update:
-      <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 16, 2022</span>
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how-to-guides/handle-training-data/handle-training-data.md

@@ -7,7 +7,7 @@ In order to simplify your training data management, `great-ai` provide two compl
 At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call [great_ai.add_ground_truth][]. This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, [great_ai.query_ground_truth][] can be called to get a filtered view of the training data.
 
 !!! important "Train-test-validation splits"
-    It is a best-practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it.
+    It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it.
     
     With [great_ai.add_ground_truth][], there is an option to tag the samples with `train`, `test`, and `validation` randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags.
 

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 <p>At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call <a class="autorefs autorefs-internal" href="../../reference/#great_ai.add_ground_truth">great_ai.add_ground_truth</a>. This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, <a class="autorefs autorefs-internal" href="../../reference/#great_ai.query_ground_truth">great_ai.query_ground_truth</a> can be called to get a filtered view of the training data.</p>
 <div class="admonition important">
 <p class="admonition-title">Train-test-validation splits</p>
-<p>It is a best-practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it.</p>
+<p>It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it.</p>
 <p>With <a class="autorefs autorefs-internal" href="../../reference/#great_ai.add_ground_truth">great_ai.add_ground_truth</a>, there is an option to tag the samples with <code>train</code>, <code>test</code>, and <code>validation</code> randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags.</p>
 </div>
 <p>The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces.</p>
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 <p>Lastly, all version of the remote object can be deleted by calling <code>LargeFileS3("my-file").delete()</code>. It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.</p>
 </details>
 <h2 id="from-the-command-line">From the command-line<a class="headerlink" href="#from-the-command-line" title="Permanent link">#</a></h2>
-<p>The main reason for using the <code>large-file</code> or <code>python3 -m great_ai.large_file</code> commands is to upload or download models from the terminal. For example, when building a docker image, it is best-practice to cache the referred models.</p>
+<p>The main reason for using the <code>large-file</code> or <code>python3 -m great_ai.large_file</code> commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models.</p>
 <h3 id="setup">Setup<a class="headerlink" href="#setup" title="Permanent link">#</a></h3>
 <p>Create an .ini file (or use <em>~/.aws/credentials</em>). It may look like this:</p>
 <div class="highlight"><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="na">aws_region_name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">your_region_like_eu-west-2</span><span class="w"></span>
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