docs: rename "VAST Data Platform" to "VAST AI OS"

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-<img src='https://github.com/vast-data/Orbit/blob/main/assets/img/vo_logo.png' width="480px">
+<img src='https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/vo_logo.png' width="480px">
 </p>
 
 > **Beta:** VAST Orbit `0.1.x` is the first beta release series. The API and features will change as we work toward a stable `1.0.0`. See [Project Status & Roadmap](#project-status--roadmap).
@@ -13,16 +13,12 @@
 [![codecov](https://codecov.io/gh/vast-data/vastorbit/branch/main/graph/badge.svg?token=a6GiFYI9at)](https://codecov.io/gh/vast-data/vastorbit)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/pylint-dev/pylint)
+[![Docs](https://img.shields.io/badge/docs-vast--data.github.io-1FD9FE)](https://vast-data.github.io/Orbit/)
 [![Slack](https://img.shields.io/badge/Slack-VAST%20Support-4A154B?logo=slack&logoColor=white)](https://vastsupport.slack.com)
 
 ## Trailer Video
 
-<p align="center">
-  <a href="https://github.com/vast-data/Orbit/blob/main/docs/source/_static/website/video/vastorbit_trailer_med.mp4">
-    <img src="https://github.com/vast-data/Orbit/blob/main/docs/source/_static/website/video/vastorbit_intro_poster.png"
-         width="100%" alt="VAST Orbit — Trailer (click to watch)">
-  </a>
-</p>
+<p align="center"><a href="https://raw.githubusercontent.com/vast-data/Orbit/main/docs/source/_static/website/video/vastorbit_trailer_med.mp4">▶ Watch the trailer</a></p>
 
 VAST Orbit is a Python library with scikit-learn-like functionality for conducting data science projects on data stored in VAST DataBase. Train models using familiar ``scikit-learn`` syntax and deploy them directly in the database, leveraging VAST's high-performance analytics capabilities. VAST Orbit offers robust support for the entire data science life cycle, uses a 'pipeline' mechanism to sequentialize data transformation operations, and provides beautiful graphical options.
 
@@ -61,7 +57,7 @@ Python has become the lingua franca of data science, offering unparalleled flexi
 
 <p align="center">
 <!-- TODO: Add architecture diagram -->
-<img src='https://github.com/vast-data/Orbit/blob/main/assets/img/architecture.png' width="100%">
+<img src='https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/architecture.png' width="100%">
 </p>
 
 ## Project Status & Roadmap
@@ -132,7 +128,7 @@ help(vo.VastFrame)
 
 Documentation can be generated locally. Refer to the documentation generation guide in the `docs/` directory.
 
-Official documentation will be available soon at a dedicated documentation site.
+Full documentation, API reference, and examples are available at [vast-data.github.io/Orbit](https://vast-data.github.io/Orbit/).
 
 ## Highlighted Features
 
@@ -144,14 +140,14 @@ Dark mode, ideal for extended coding sessions, features a sleek and stylish dark
 
 <p align="center">
 <!-- TODO: Add dark theme screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/dark-theme-screenshot.png" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/dark-theme-screenshot.png" width="100%">
 </p>
 
 On the other hand, Light mode serves as the default theme, offering a clean and bright interface for users who prefer a traditional coding ambiance.
 
 <p align="center">
 <!-- TODO: Add light theme screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/light-theme-screenshot.png" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/light-theme-screenshot.png" width="100%">
 </p>
 
 Theme can be easily switched by:
@@ -185,7 +181,7 @@ SELECT version();
 
 <p align="center">
 <!-- TODO: Add light theme screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/sql_version.png" width="15%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/sql_version.png" width="15%">
 </p>
 
 ### SQL Plots
@@ -203,7 +199,7 @@ To create plots, simply provide the type of plot along with the SQL command.
 
 <p align="center">
 <!-- TODO: Add SQL plot screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/sql-plot-screenshot.png" width="90%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/sql-plot-screenshot.png" width="90%">
 </p>
 
 ### Python and SQL Combo
@@ -223,7 +219,7 @@ selected_titanic.groupby(columns=["pclass"], expr=["AVG(survived) AS avg_survive
 
 <p align="center">
 <!-- TODO: Add SQL plot screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/select_gb.png" width="30%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/select_gb.png" width="30%">
 </p>
 
 ### Charts
@@ -234,7 +230,7 @@ A gallery of VAST Orbit-generated charts will be available in the documentation.
 
 <p align="center">
 <!-- TODO: Add charts gallery screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/charts-gallery.gif" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/charts-gallery.gif" width="100%">
 </p>
 
 ### Complete Machine Learning Pipeline
@@ -286,7 +282,7 @@ iris_data.scatter(
 
 <p align="center">
 <!-- TODO: Add scatter plot screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/scatter-plot.png" width="70%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/scatter-plot.png" width="70%">
 </p>
 
 The **Correlation Matrix** is fast and convenient to compute. Users can choose from a wide variety of correlations, including Cramer, Spearman, Pearson, etc.
@@ -300,7 +296,7 @@ titanic.corr(method="spearman")
 
 <p align="center">
 <!-- TODO: Add correlation matrix screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/correlation-matrix.png" width="70%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/correlation-matrix.png" width="70%">
 </p>
 
 By turning on the SQL print option, users can see and copy SQL queries:
@@ -319,7 +315,7 @@ titanic.corr(method="spearman", focus="survived")
 
 <p align="center">
 <!-- TODO: Add focused correlation screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/focused-correlation.png" width="50%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/focused-correlation.png" width="50%">
 </p>
 
 #### Data Preparation
@@ -338,7 +334,7 @@ data.outliers_plot(columns="Heights")
 
 <p align="center">
 <!-- TODO: Add outliers plot screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/outliers-plot.png" width="70%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/outliers-plot.png" width="70%">
 </p>
 
 #### Machine Learning
@@ -372,7 +368,7 @@ cross_validate(
 
 <p align="center">
 <!-- TODO: Add model evaluation screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/model-evaluation.png" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/model-evaluation.png" width="100%">
 </p>
 
 ### Loading Predefined Datasets
@@ -391,7 +387,7 @@ iris_data = load_iris()
 
 <p align="center">
 <!-- TODO: Add model evaluation screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/iris-dataset.png" width="70%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/iris-dataset.png" width="70%">
 </p>
 
 (2) Use the standard name of the dataset from the schema:
@@ -404,7 +400,7 @@ iris_data = vo.VastFrame(input_relation="public.iris")
 
 <p align="center">
 <!-- TODO: Add model evaluation screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/iris-dataset.png" width="70%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/iris-dataset.png" width="70%">
 </p>
 
 ## Quickstart
@@ -452,7 +448,7 @@ vdf = load_titanic()
 
 <p align="center">
 <!-- TODO: Add model evaluation screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/light-theme-screenshot.png" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/light-theme-screenshot.png" width="100%">
 </p>
 
 Examine your data:
@@ -463,7 +459,7 @@ vdf.describe()
 
 <p align="center">
 <!-- TODO: Add describe output screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/describe-output.png" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/describe-output.png" width="100%">
 </p>
 
 Print the SQL query with `set_option`:
@@ -513,7 +509,7 @@ cross_validate(
 
 <p align="center">
 <!-- TODO: Add cross-validation results screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/cross-validation.png" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/cross-validation.png" width="100%">
 </p>
 
 Train and deploy the model:
@@ -532,7 +528,7 @@ model.features_importance()
 
 <p align="center">
 <!-- TODO: Add feature importance screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/feature-importance.png" width="100%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/feature-importance.png" width="100%">
 </p>
 
 ROC Curve:
@@ -544,7 +540,7 @@ model.roc_curve()
 
 <p align="center">
 <!-- TODO: Add ROC curve screenshot -->
-<img src="https://github.com/vast-data/Orbit/blob/main/assets/img/roc-curve.png" width="70%">
+<img src="https://raw.githubusercontent.com/vast-data/Orbit/main/assets/img/roc-curve.png" width="70%">
 </p>
 
 Once trained, the model can be deployed in the database for high-performance predictions _(in-database deployment availability and limitations vary by algorithm — see [Project Status & Roadmap](#project-status--roadmap))_.

docs/source/_templates/page.html

@@ -45,7 +45,7 @@
       <a class="vast-footer-wordmark" href="{{ pathto(master_doc) }}">VAST&nbsp;<span>Orbit</span></a>
       <p class="vast-footer-tagline">
         In-database analytics for Python — explore, model and visualize data
-        at scale, directly on the VAST Data Platform.
+        at scale, directly on the VAST AI OS.
       </p>
       <div class="vast-footer-social">
         <a href="https://github.com/vast-data/Orbit" aria-label="GitHub">

docs/source/about_us.rst

@@ -31,7 +31,7 @@ The story behind VAST Orbit
         thinks. Meanwhile the data spread across databases, lakes, and files, and
         reaching all of it meant building yet another pipeline.
 
-        The VAST Data Platform removed the usual reason for all that movement by
+        The VAST AI OS removed the usual reason for all that movement by
         unifying transactional and analytical processing, all-flash performance at
         data-lake economics, and linear scale from gigabytes to exabytes. What was
         still missing was a way for data scientists to use that power with the Python
@@ -130,6 +130,16 @@ The team
 
           Principal Systems Engineer
 
+    .. grid-item::
+
+        .. card:: Maciej Paliwoda
+          :img-top: _static/website/about_us/team/member7.jpg
+          :link: https://www.linkedin.com/in/maciejpaliwoda/
+          :text-align: center
+          :class-card: member-pics-card
+
+          Senior Systems Engineer
+
     .. grid-item::
 
         .. card:: Kuldeep Venati
@@ -187,5 +197,5 @@ tracker above.
 
 .. note::
 
-    VAST Orbit brings Python data science to the VAST Data Platform: prepare,
+    VAST Orbit brings Python data science to the VAST AI OS: prepare,
     explore, analyze, and build AI — all with in-database execution at any scale.

docs/source/api.rst

@@ -17,7 +17,7 @@ API Reference
         </div>
     </div>
 
-Welcome to the VAST Orbit API Reference. This comprehensive guide covers all public objects, functions, and methods available in VAST Orbit for data science at scale on the VAST Data Platform.
+Welcome to the VAST Orbit API Reference. This comprehensive guide covers all public objects, functions, and methods available in VAST Orbit for data science at scale on the VAST AI OS.
 
 .. tip::
 

docs/source/chart_gallery_geo.rst

@@ -44,7 +44,7 @@ Let's utilize the World dataset to demonstrate geospatial capabilities.
     africa = world[world["continent"] == "Africa"]
 
 Let's use Africa Education dataset from the vastorbit datasets. 
-Data is also available `here <https://github.com/vast-data/Orbit/blob/main/assets/data/understand/africa_education/students.csv>`__.
+Data is also available `here <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/understand/africa_education/students.csv>`__.
 
 .. code-block:: python
 

docs/source/cicd.rst

@@ -213,7 +213,7 @@ For Contributors
 
 .. tip::
 
-   Set up pre-commit hooks to run checks automatically before each commit. See our `contributing guide <https://github.com/vast-data/Orbit/blob/main/CONTRIBUTING.md>`__ for setup instructions.
+   Set up pre-commit hooks to run checks automatically before each commit. See our `contributing guide <https://raw.githubusercontent.com/vast-data/Orbit/main/CONTRIBUTING.md>`__ for setup instructions.
 
 ____
 

docs/source/connection.rst

@@ -6,9 +6,9 @@ Connection
 
 .. include:: logo_include.rst
 
-**Connecting to VAST Data Platform with VAST Orbit**
+**Connecting to VAST AI OS with VAST Orbit**
 
-VAST Orbit connects to the VAST Data Platform through Trino today, a powerful distributed SQL query engine (VAST's own engine is coming). This connection unlocks access to VAST DataBase tables, data lake files, and any other Trino-supported data source - all through one unified Python API.
+VAST Orbit connects to the VAST AI OS through Trino today, a powerful distributed SQL query engine (VAST's own engine is coming). This connection unlocks access to VAST DataBase tables, data lake files, and any other Trino-supported data source - all through one unified Python API.
 
 .. important::
 
@@ -625,4 +625,4 @@ power of VAST Orbit for federated analytics and AI development.
    - :ref:`getting_started` - Quick start guide
    - :ref:`user_guide` - VastFrame operations and federated queries
    - `Trino Documentation <https://trino.io/docs/current/>`__ - Trino reference
-   - `VAST Data Platform <https://www.vastdata.com/platform/database>`__ - VAST overview
+   - `VAST AI OS <https://www.vastdata.com/platform/database>`__ - VAST overview

docs/source/examples.rst

@@ -60,7 +60,7 @@ ____
 
 .. tip::
 
-   All notebooks run on the VAST Data Platform. Download and execute them with
+   All notebooks run on the VAST AI OS. Download and execute them with
    your own data!
 
 .. toctree::

docs/source/examples_business.rst

@@ -6,7 +6,7 @@ Business Examples
 
 .. include:: logo_include.rst
 
-Real-world ML applications on VAST Data Platform - from fraud detection to predictive maintenance.
+Real-world ML applications on VAST AI OS - from fraud detection to predictive maintenance.
 
 ____
 

docs/source/examples_business_churn.rst

@@ -31,7 +31,7 @@ You can skip the below cell if you already have an established connection.
 
     vo.connect("VASTDSN")
 
-Let's create a VastFrame of the dataset. The dataset is available `here <https://github.com/vast-data/Orbit/blob/main/assets/data/business/churn/customers.csv>`__.
+Let's create a VastFrame of the dataset. The dataset is available `here <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/business/churn/customers.csv>`__.
 
 .. code-block:: ipython
 

docs/source/examples_business_football.rst

@@ -3,7 +3,7 @@
 Football
 =========
 
-In this example, we use the ``football`` dataset to predict the outcomes of games between various teams. You can download the dataset `here <https://github.com/vast-data/Orbit/blob/main/assets/data/business/football/games.csv>`__.
+In this example, we use the ``football`` dataset to predict the outcomes of games between various teams. You can download the dataset `here <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/business/football/games.csv>`__.
 
 - **date:** Date of the game.
 - **home_team:** Home Team.

docs/source/examples_business_smart_meters.rst

@@ -5,19 +5,19 @@ Smart Meters
 
 This example uses the following datasets to predict peoples' electricity consumption. We'll use the following datasets:
 
-`sm_consumption <https://github.com/vast-data/Orbit/blob/main/assets/data/business/smart_meters/sm_consumption.csv>`__
+`sm_consumption <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/business/smart_meters/sm_consumption.csv>`__
 
 - **dateUTC:** Date and time of the record.
 - **meterID:** Smart meter ID.
 - **value:** Electricity consumed during 30 minute interval (in kWh).
 
-`sm_weather <https://github.com/vast-data/Orbit/blob/main/assets/data/business/smart_meters/sm_weather.csv>`__
+`sm_weather <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/business/smart_meters/sm_weather.csv>`__
 
 - **dateUTC:** Date and time of the record.
 - **temperature:** Temperature.
 - **humidity:** Humidity.
 
-`sm_meters <https://github.com/vast-data/Orbit/blob/main/assets/data/business/smart_meters/sm_meters.csv>`__
+`sm_meters <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/business/smart_meters/sm_meters.csv>`__
 
 - **longitude:** Longitude.
 - **latitude:** Latitude.

docs/source/examples_business_spam.rst

@@ -29,7 +29,7 @@ You can skip the below cell if you already have an established connection.
 
     vo.connect("VASTDSN")
 
-Let's create a VastFrame of the dataset. The dataset is available `here <https://github.com/vast-data/Orbit/blob/main/assets/data/business/spam/spam.csv>`__.
+Let's create a VastFrame of the dataset. The dataset is available `here <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/business/spam/spam.csv>`__.
 
 .. code-block:: ipython
 

docs/source/examples_understand_covid19.rst

@@ -33,7 +33,7 @@ You can skip the below cell if you already have an established connection.
 
     vo.connect("VASTDSN")
 
-Let's create a VastFrame of the dataset. The dataset is available `here <https://github.com/vast-data/Orbit/blob/main/assets/data/understand/covid19/deaths.csv>`__.
+Let's create a VastFrame of the dataset. The dataset is available `here <https://raw.githubusercontent.com/vast-data/Orbit/main/assets/data/understand/covid19/deaths.csv>`__.
 
 .. code-block:: python
 

docs/source/getting_started.rst

@@ -7,7 +7,7 @@ Getting Started
 .. include:: logo_include.rst
 
 VAST Orbit is the open-source Python library for in-database data science on the
-VAST Data Platform. This guide takes you from an empty environment to your first
+VAST AI OS. This guide takes you from an empty environment to your first
 in-database query: install the library, connect to VAST, and prepare,
 explore, and model your data without ever moving it.
 
@@ -275,5 +275,5 @@ library.
 
 .. note::
 
-   VAST Orbit brings Python data science to the VAST Data Platform: prepare, explore,
+   VAST Orbit brings Python data science to the VAST AI OS: prepare, explore,
    analyze, and build AI - all with in-database execution at any scale.