Update docs to add on-premise message.

README.md

@@ -197,6 +197,10 @@ You can then **upload the test suite** created using the `giskard` Python librar
     test_suite.upload(client, "my_project")
     ```
 
+> **The Giskard server is installed on your infrastructure.**
+>
+> Giskard as a company does not have access to your datasets and models, so you can keep everything private.
+
 # ❓ Where can I get more help?
 
 

docs/getting-started/index.md

@@ -29,3 +29,11 @@ At Giskard, we believe that Machine Learning needs its own testing framework. Cr
 <br>
 
   ![](/_static/catalog_example.png)
+
+<br>
+
+:::{info}
+**The Giskard server is installed on your infrastructure.**
+
+Giskard as a company does not have access to your datasets and models, so you can keep everything private.
+:::

docs/getting-started/quickstart.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "990eccb8",
    "metadata": {},
@@ -15,6 +16,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "40f23d1a",
    "metadata": {},
@@ -62,6 +64,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -3167,6 +3170,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "0667bdc9",
    "metadata": {},
@@ -3190,6 +3194,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "882f4638",
    "metadata": {},
@@ -3198,6 +3203,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "502a0767",
    "metadata": {},
@@ -3228,6 +3234,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cf824254",
    "metadata": {},
@@ -3269,6 +3276,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -3288,6 +3296,19 @@
     "Read the flowing [instructions](https://docs.giskard.ai/en/latest/cli/ngrok/index.html) in order to get the `ngrok_API_token`. This will provide you with the code snippets that you can copy and paste into your Colab notebook to establish a connection with your locally installed Giskard server\n",
     "</div>"
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e509f755",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "The Giskard server is installed on your infrastructure\n",
+    "\n",
+    "Giskard as a company does not have access to your datasets and models, so you can keep everything private.\n",
+    "</div>"
+   ]
   }
  ],
  "metadata": {

docs/guides/installation_app/index.rst

@@ -1,4 +1,4 @@
-🌐 Run the Giskard Server
+🌐 Run the Giskard Server (on-premise)
 ===============
 
 Complementing the `Giskard Python library <../installation_library/index.md>`_, the Giskard server is the app that you can install either **locally**, on your **cloud instance** or on `Hugging Face Spaces <install_hfs/index.md>`_. The Giskard server offers a bunch of features such as:

docs/guides/upload/index.md

@@ -1,7 +1,9 @@
-# ⬆️ Upload an object to the Giskard server
+# ⬆️ Log datasets & models in the Giskard server
 
 You can easily upload to the Giskard server objects, such as **model**, **dataset**, **slicing & transformation functions** and **test suite**.
 
+The Giskard server is installed on your infrastructure. Giskard as a company does not have access to your datasets and models, so you can keep everything private.
+
 Uploading objects to the Giskard server will help you:
 
 * **Debug** your model by inspecting the rows that make your test fail