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Make documentation links usable from repository previews

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Andras Schmelczer 2026-10-27 21:59:39 +00:00
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docs/examples/simple/data.ipynb
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@ -8,10 +8,10 @@
"\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",
"![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-data.svg)\n",
"> The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook.\n",
"\n",
"Since the true scope of `great-ai` is the phase between proof-of-concept code and production-ready service, it is predominantly used in the [deployment notebook](/examples/simple/deploy). Feel free to skip there, or continue reading if you'd like to see the full picture.\n",
"Since the true scope of `great-ai` is the phase between proof-of-concept code and production-ready service, it is predominantly used in the [deployment notebook](https://great-ai.scoutinscience.com/examples/simple/deploy). Feel free to skip there, or continue reading if you'd like to see the full picture.\n",
"\n",
"### Extract\n",
"\n",
@ -199,7 +199,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next: [Part 2](/examples/simple/train)"
"### Next: [Part 2](https://great-ai.scoutinscience.com/examples/simple/train)"
]
}
],