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Andras Schmelczer 2026-10-28 12:57:37 +00:00
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6
docs/tutorial/deploy.ipynb
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@ -123,11 +123,11 @@
"\n",
"Your service now has tracing and API scaffolding. Before deploying, replace the default local TinyDB tracing backend, configure storage and infrastructure, evaluate the model for its intended use, and review the remaining best practices. Model trustworthiness requires evidence and ongoing oversight beyond wrapping the inference function. You (or your organisation) remain responsible for deploying and operating the service.\n",
"\n",
"There are three main approaches to deploy a GreatAI service: For more info about them, check out [the deployment how-to](https://great-ai.scoutinscience.com/how-to-guides/use-service).\n",
"There are three main approaches to deploy a GreatAI service: For more info about them, check out [the deployment how-to](https://great-ai.schmelczer.dev/how-to-guides/use-service).\n",
"\n",
"For more thorough examples, see the [examples page](https://great-ai.scoutinscience.com/examples/simple/data).\n",
"For more thorough examples, see the [examples page](https://great-ai.schmelczer.dev/examples/simple/data).\n",
"\n",
"### [Go back to the summary](https://great-ai.scoutinscience.com/tutorial/#summary)"
"### [Go back to the summary](https://great-ai.schmelczer.dev/tutorial/#summary)"
],
"id": "cell-7"
}

12
docs/tutorial/index.md
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@ -4,8 +4,8 @@ Let's see `great-ai` in action by going over the lifecycle of a simple service.
## Objectives
1. You will see how the [great_ai.utilities](https://great-ai.scoutinscience.com/reference/utilities) can integrate into your Data Science workflow.
2. You will use [great_ai.large_file](https://great-ai.scoutinscience.com/reference/large-file) to version and store your trained model.
1. You will see how the [great_ai.utilities](https://great-ai.schmelczer.dev/reference/utilities) can integrate into your Data Science workflow.
2. You will use [great_ai.large_file](https://great-ai.schmelczer.dev/reference/large-file) to version and store your trained model.
3. You will use [GreatAI][great_ai.GreatAI] to add tracing and API scaffolding to your inference function, then review the remaining deployment requirements.
## Overview
@ -52,7 +52,7 @@ After training and evaluating a model, it is exported using [great_ai.save_model
save_model(model, key='my-domain-predictor')
```
For more info, check out [the configuration how-to page](https://great-ai.scoutinscience.com/how-to-guides/configure-service).
For more info, check out [the configuration how-to page](https://great-ai.schmelczer.dev/how-to-guides/configure-service).
### [Deployment notebook](deploy.ipynb)
@ -72,10 +72,10 @@ def predict_domain(sentence, model):
1. [@use_model][great_ai.use_model] loads and injects your model into the `predict_domain` function's `model` argument.
You can freely reference it, knowing that the function is always provided with it.
Finally, we test the model's inference function through the GreatAI dashboard. Before production, configure a suitable tracing database and storage, evaluate the model for its intended use, review the remaining best practices, and [deploy the service](https://great-ai.scoutinscience.com/how-to-guides/use-service).
Finally, we test the model's inference function through the GreatAI dashboard. Before production, configure a suitable tracing database and storage, evaluate the model for its intended use, review the remaining best practices, and [deploy the service](https://great-ai.schmelczer.dev/how-to-guides/use-service).
<div style="display: flex; justify-content: space-evenly;" markdown>
[:material-book: Learn about all the features](https://great-ai.scoutinscience.com/how-to-guides/create-service){ .md-button .md-button--primary }
[:material-book: Learn about all the features](https://great-ai.schmelczer.dev/how-to-guides/create-service){ .md-button .md-button--primary }
[:material-test-tube: Look at more examples](https://great-ai.scoutinscience.com/examples/simple/data){ .md-button .md-button--secondary }
[:material-test-tube: Look at more examples](https://great-ai.schmelczer.dev/examples/simple/data){ .md-button .md-button--secondary }
</div>

6
docs/tutorial/train.ipynb
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@ -68,7 +68,7 @@
"source": [
"First, we have to get some data. After downloading it [from here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag), we might notice that the dataset is in [JSON Lines](https://jsonlines.org/) format (each line is a separate JSON document). \n",
"\n",
"Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also [cleaned](https://great-ai.scoutinscience.com/reference/utilities/#great_ai.utilities.clean.clean) to remove any LaTeX, XML, unicode, PDF-extraction artifacts."
"Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also [cleaned](https://great-ai.schmelczer.dev/reference/utilities/#great_ai.utilities.clean.clean) to remove any LaTeX, XML, unicode, PDF-extraction artifacts."
],
"id": "cell-4"
},
@ -96,7 +96,7 @@
"cell_type": "markdown",
"metadata": {},
"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`](https://great-ai.scoutinscience.com/reference/utilities/#great_ai.utilities.simple_parallel_map) function.\n",
"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`](https://great-ai.schmelczer.dev/reference/utilities/#great_ai.utilities.simple_parallel_map) function.\n",
"\n",
"> Open files in Python are iterable: in text mode, each iteration returns the next line."
],
@ -242,7 +242,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### [Let's continue by finishing the deployment in the next notebook.](https://great-ai.scoutinscience.com/tutorial/deploy)"
"### [Let's continue by finishing the deployment in the next notebook.](https://great-ai.schmelczer.dev/tutorial/deploy)"
],
"id": "cell-18"
}