Clarify production readiness and model trustworthiness

README.md

@@ -1,6 +1,6 @@
 # <img src="https://raw.githubusercontent.com/schmelczer/great-ai/main/docs/media/logo.png" alt="logo of great-ai" width=60 /> GreatAI
 
-> Easily transform your prototype AI code into production-ready software.
+> Prepare your prototype AI code for production deployment.
 
 [![PyPI version](https://badge.fury.io/py/great-ai.svg)](https://badge.fury.io/py/great-ai)
 [![Downloads](https://pepy.tech/badge/great-ai/month)](https://pepy.tech/project/great-ai)
@@ -9,7 +9,7 @@
 [![Sonar line coverage](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=coverage)](https://sonar.scoutinscience.com/dashboard?id=great-ai)
 [![Sonar LoC](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=ncloc)](https://sonar.scoutinscience.com/dashboard?id=great-ai)
 
-Applying AI is becoming increasingly more accessible, but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases. GreatAI helps fix this by allowing you to easily transform your prototype AI code into production-ready software.
+Applying AI is becoming increasingly more accessible, but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases. GreatAI helps prepare prototype AI code for deployment with tracing, model versioning, and API scaffolding. These engineering features support oversight; they do not establish a model's accuracy, fairness, or trustworthiness, which require evaluation for the intended use case.
 
 ## Example
 
@@ -33,6 +33,8 @@ Start it by executing `great-ai demo.py`, and find the dashboard at [http://loca
 
 That's it. Your GreatAI service is _nearly_ ready for production use. Many of the [SE4ML best practices](https://se-ml.github.io) are configured and implemented automatically (of course, these can be customised as well).
 
+Before production, replace the default local TinyDB tracing database with a production-suitable backend, configure storage and deployment infrastructure, and review the remaining best practices for your use case.
+
 [Check out the full documentation here](https://great-ai.scoutinscience.com).
 
 ## Why is this GREAT?
@@ -45,7 +47,7 @@ GreatAI fits between the prototype and deployment phases of your AI development
 - **R**obust: have error-handling and well-tested utilities out-of-the-box
 - **E**nd-to-end: utilise end-to-end feedback as a built-in, first-class concept
 - **A**utomated: focus only on what actually requires your attention
-- **T**rustworthy: deploy models that you and society can confidently trust
+- **T**rustworthy: support model assessment and oversight with traces and feedback; trustworthiness requires use-case-specific evaluation
 
 ## Why GreatAI?
 

docs/index.md

@@ -10,7 +10,7 @@
 [![Sonar line coverage](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=coverage)](https://sonar.scoutinscience.com/dashboard?id=great-ai)
 [![Sonar LoC](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=ncloc)](https://sonar.scoutinscience.com/dashboard?id=great-ai)
 
-Applying AI is becoming increasingly easier, but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing [unintended biases](https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction){ target=_blank }. GreatAI helps fix this by allowing you to ==easily transform your prototype AI code into production-ready software==.
+Applying AI is becoming increasingly easier, but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing [unintended biases](https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction){ target=_blank }. GreatAI helps ==prepare prototype AI code for deployment with tracing, model versioning, and API scaffolding==. These engineering features support oversight; they do not establish a model's accuracy, fairness, or trustworthiness, which require evaluation for the intended use case.
 
 ??? quote "Case studies"
     "There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature."
@@ -82,7 +82,9 @@ great-ai demo.py
 ![demo screen capture](media/demo.gif){ loading=lazy }
 
 !!! success
-    Your GreatAI service is ready for production use. Many of the [SE4ML best practices](https://se-ml.github.io){ target=_blank } are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.
+    Your GreatAI service is _nearly_ ready for production use. Many of the [SE4ML best practices](https://se-ml.github.io){ target=_blank } are configured and implemented automatically.
+
+    Before production, replace the default local TinyDB tracing database with a production-suitable backend, [configure storage](https://great-ai.scoutinscience.com/how-to-guides/configure-service/) and [deployment infrastructure](https://great-ai.scoutinscience.com/how-to-guides/use-service/), and review the remaining best practices for your use case. Wrapping a function provides tracing and API scaffolding; model trustworthiness still requires use-case-specific evaluation and ongoing oversight.
 
 ## Why is this GREAT?
 
@@ -94,7 +96,7 @@ GreatAI fits between the prototype and deployment phases of your (or your organi
 - **R**obust: have error-handling and well-tested utilities out-of-the-box 
 - **E**nd-to-end: utilise end-to-end feedback as a built-in, first-class concept
 - **A**utomated: focus only on what actually requires your attention
-- **T**rustworthy: deploy models that you and society can confidently trust
+- **T**rustworthy: support model assessment and oversight with traces and feedback; trustworthiness requires use-case-specific evaluation
 
 ## Why GreatAI?
 

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 }