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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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6
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)"
]
}
],

4
docs/examples/simple/deploy.ipynb
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@ -8,10 +8,10 @@
"\n",
"> Part 3: Create production inference function\n",
"\n",
"![position of this step in the lifecycle](/media/scope-deploy.svg)\n",
"![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-deploy.svg)\n",
"> The blue boxes show the steps implemented in this notebook.\n",
"\n",
"In [Part 2](/examples/simple/train), we trained our AI model. Now, it's time to create **G**eneral **R**obust **E**nd-to-end **A**utomated **T**rustworthy deployment.\n",
"In [Part 2](https://great-ai.scoutinscience.com/examples/simple/train), we trained our AI model. Now, it's time to create **G**eneral **R**obust **E**nd-to-end **A**utomated **T**rustworthy deployment.\n",
"\n",
"## Create the inference function\n",
"\n",

6
docs/examples/simple/train.ipynb
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@ -6,14 +6,14 @@
"source": [
"# Optimise and train a model\n",
"\n",
"![position of this step in the lifecycle](/media/scope-train.svg)\n",
"![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-train.svg)\n",
"> The blue boxes show the steps implemented in this notebook.\n",
"\n",
"In the first part, we have cleaned and transformed our training data. We can now access this data using `great_ai.LargeFile`. Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. \n",
"\n",
"In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using `great_ai.save_model`.\n",
"\n",
"## Load data that has been extracted in [part 1](/examples/simple/data)"
"## Load data that has been extracted in [part 1](https://great-ai.scoutinscience.com/examples/simple/data)"
]
},
{
@ -861,7 +861,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next: [Part 3](/examples/simple/deploy)"
"## Next: [Part 3](https://great-ai.scoutinscience.com/examples/simple/deploy)"
]
}
],