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Andras Schmelczer 2026-10-28 12:57:37 +00:00
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2
docs/examples/simple/data.ipynb
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@ -185,7 +185,7 @@
"id": "cell-10",
"metadata": {},
"source": [
"### Next: [Part 2](https://great-ai.scoutinscience.com/examples/simple/train)"
"### Next: [Part 2](https://great-ai.schmelczer.dev/examples/simple/train)"
]
}
],

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docs/examples/simple/deploy.ipynb
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@ -9,10 +9,10 @@
"\n",
"> Part 3: Create production inference function\n",
"\n",
"![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-deploy.svg)\n",
"![position of this step in the lifecycle](https://great-ai.schmelczer.dev/media/scope-deploy.svg)\n",
"> The blue boxes show the steps implemented in this notebook.\n",
"\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",
"In [Part 2](https://great-ai.schmelczer.dev/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",

8
docs/examples/simple/train.ipynb
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@ -7,14 +7,14 @@
"source": [
"# Optimise and train a model\n",
"\n",
"![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-train.svg)\n",
"![position of this step in the lifecycle](https://great-ai.schmelczer.dev/media/scope-train.svg)\n",
"> The blue boxes show the steps implemented in this notebook.\n",
"\n",
"Run `data.ipynb` first. We read its ground truth from the shared local TinyDB database. The default uses a compact parameter search suitable for the bundled sample; `GREAT_AI_NOTEBOOK_FULL=1` enables the original larger search on the full MAG training data. \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](https://great-ai.scoutinscience.com/examples/simple/data)"
"## Load data that has been extracted in [part 1](https://great-ai.schmelczer.dev/examples/simple/data)"
]
},
{
@ -138,7 +138,7 @@
"\n",
"Each paper may have several accepted labels. We expand it into one training row per label for this single-label classifier, but keep all rows with the same paper text in one cross-validation fold using `GroupKFold`. The final train/test split was already made by paper in Part 1, before expansion.\n",
"\n",
"The search uses macro F1 on the expanded single-label validation rows as a training surrogate. This differs from the top-1 hit rate and fixed-target multilabel metrics used to evaluate the deployed label sets in [Part 3](https://great-ai.scoutinscience.com/examples/simple/deploy). Rerun the search to obtain results for these grouped folds; the previous ungrouped outputs have been removed."
"The search uses macro F1 on the expanded single-label validation rows as a training surrogate. This differs from the top-1 hit rate and fixed-target multilabel metrics used to evaluate the deployed label sets in [Part 3](https://great-ai.schmelczer.dev/examples/simple/deploy). Rerun the search to obtain results for these grouped folds; the previous ungrouped outputs have been removed."
]
},
{
@ -2783,7 +2783,7 @@
"id": "cell-11",
"metadata": {},
"source": [
"## Next: [Part 3](https://great-ai.scoutinscience.com/examples/simple/deploy)"
"## Next: [Part 3](https://great-ai.schmelczer.dev/examples/simple/deploy)"
]
}
],