Clarify production readiness and model trustworthiness

docs/tutorial/deploy.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment [best practices](https://se-ml.github.io/). In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored.\n",
     "\n",
-    "With `GreatAI`, it has become a matter of 4 lines of code."
+    "GreatAI's decorators add tracing and API scaffolding with a few lines of code. This brings the service nearly to production readiness, but does not establish the model's accuracy, fairness, or trustworthiness. Evaluate the model for its intended use, configure a production-suitable tracing database and storage, and review the remaining deployment best practices. The local TinyDB backend used below is for development, as the output warns."
    ]
   },
   {
@@ -139,7 +139,7 @@
    "source": [
     "Congrats, you've just created your first GreatAI service! 🎉\n",
     "\n",
-    "Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services.\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](/how-to-guides/use-service).\n",
     "\n",

docs/tutorial/index.md

@@ -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 }