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2
tutorial/deploy/deploy.ipynb
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@ -139,7 +139,7 @@
"source": [
"Congrats, you've just created your first GreatAI service! 🎉\n",
"\n",
"Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already has an established routine for deploying services.\n",
"Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services.\n",
"\n",
"There are three main approaches to deploy a GreatAI service: For more info about them, check out [the deployment how-to](/how-to-guides/use-service).\n",
"\n",

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tutorial/deploy/index.html
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@ -451,7 +451,7 @@
<li class="md-nav__item">
<a href="../../how-to-guides/use-service/" class="md-nav__link">
How to deploy a GreatAI service
How to perform prediction with GreatAI
</a>
</li>
@ -2091,7 +2091,7 @@ def predict_domain(sentence, model):
<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">
<p>Congrats, you've just created your first GreatAI service! 🎉</p>
<p>Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already has an established routine for deploying services.</p>
<p>Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services.</p>
<p>There are three main approaches to deploy a GreatAI service: For more info about them, check out <a href="/how-to-guides/use-service">the deployment how-to</a>.</p>
<p>For more thorough examples, see the <a href="/examples/simple/data">examples page</a>.</p>
<h3 id="go-back-to-the-summary"><a href="/tutorial/#summary">Go back to the summary</a><a class="anchor-link" href="#go-back-to-the-summary">&#182;</a></h3>
@ -2108,7 +2108,7 @@ def predict_domain(sentence, model):
<small>
Last update:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 12, 2022</span>
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 13, 2022</span>
</small>

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tutorial/index.html
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@ -485,7 +485,7 @@
<li class="md-nav__item">
<a href="../how-to-guides/use-service/" class="md-nav__link">
How to deploy a GreatAI service
How to perform prediction with GreatAI
</a>
</li>

6
tutorial/train/index.html
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@ -451,7 +451,7 @@
<li class="md-nav__item">
<a href="../../how-to-guides/use-service/" class="md-nav__link">
How to deploy a GreatAI service
How to perform prediction with GreatAI
</a>
</li>
@ -1962,7 +1962,7 @@ def preprocess(line):
<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">
<p>Now, we can load the dataset and extract the <em>training</em> samples from it. Since we're impatient, we can do it in parallel using the <a href="/reference/utilities/#great_ai.utilities.simple_parallel_map"><code>simple_parallel_map</code></a> function.</p>
<blockquote><p>Open files in Python are iterable, with a line being return (in text mode) with each iteration.</p>
<blockquote><p>Open files in Python are iterable: in text mode, each iteration returns the next line.</p>
</blockquote>
</div>
@ -2484,7 +2484,7 @@ save_model(model, key="my-domain-predictor")</div>
<small>
Last update:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 12, 2022</span>
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 13, 2022</span>
</small>

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tutorial/train/train.ipynb
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@ -60,7 +60,7 @@
"source": [
"Now, we can load the dataset and extract the *training* samples from it. Since we're impatient, we can do it in parallel using the [`simple_parallel_map`](/reference/utilities/#great_ai.utilities.simple_parallel_map) function.\n",
"\n",
"> Open files in Python are iterable, with a line being return (in text mode) with each iteration."
"> Open files in Python are iterable: in text mode, each iteration returns the next line."
]
},
{