andras/great-ai
andras/great-ai
Watch
1
0
Fork
You've already forked great-ai
 0
Code Issues Pull requests 2 Projects Releases 4 Packages 1 Wiki Activity Actions

Clarify production readiness and model trustworthiness

Browse source
This commit is contained in:
Andras Schmelczer 2026-10-27 21:59:11 +00:00
commit d2616cf997
1 changed files with 15 additions and 11 deletions
Download patch file Download diff file Expand all files Collapse all files

6
docs/tutorial/index.md
View file

@ -6,7 +6,7 @@ Let's see `great-ai` in action by going over the lifecycle of a simple service.
1. You will see how the [great_ai.utilities](/reference/utilities) can integrate into your Data Science workflow.
2. You will use [great_ai.large_file](/reference/large-file) to version and store your trained model.
3. You will use [GreatAI][great_ai.GreatAI] to prepare your model for a robust and responsible deployment.
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
@ -54,7 +54,7 @@ After training and evaluating a model, it is exported using [great_ai.save_model
### [Deployment notebook](deploy.ipynb)
We create an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance.
We wrap an inference function in a [GreatAI][great_ai.GreatAI] instance to add tracing, model loading, and API scaffolding. Evaluating the model's trustworthiness remains a separate responsibility.
```python
from great_ai import GreatAI, use_model
@ -70,7 +70,7 @@ 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. [The only thing left is to deploy the hardened service properly.](/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](/how-to-guides/use-service).
<div style="display: flex; justify-content: space-evenly;" markdown>
[:material-book: Learn about all the features](/how-to-guides/create-service){ .md-button .md-button--primary }