chore: Update README.md

README.md

@@ -1,46 +1,42 @@
-# YData SDK
-
-<p></p>
-<p align="center"><img width="500" src="https://assets.ydata.ai/sdk/logo_SDK_col_red_black.png" alt="YData Logo"></p>
-<p></p>
+# YData Fabric SDK
 
 [![pypi](https://img.shields.io/pypi/v/ydata-fabric-sdk)](https://pypi.org/project/ydata-fabric-sdk)
 ![Pythonversion](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)
 [![downloads](https://pepy.tech/badge/ydata-fabric-sdk/month)](https://pepy.tech/project/ydata-fabric-sdk)
 
 ---
-🚀 YData SDK Version 1.0 Released! 🎉 - Data quality everywhere!
+🚀 YData Fabric SDK 🎉
+Fabric's platform capabilities at the distance of a Python command!
+
 
-*ydata-fabric-sdk* v1 is here! Create a [YData Fabric account](https://ydata.ai/ydata-fabric-free-trial) so you can start using today!
+*ydata-fabric-sdk* is here! Create a [YData Fabric account](https://ydata.ai/register) so you can start using today!
 
-We are excited to announce the release of YData Fabric SDK v1.0! This major release marks the beginning of long-term support for the package,
-ensuring stability, continuous improvements, and ongoing support for all users. YData SDK empowers developers with easy access to state-of-the-art
-data quality tools and generative AI capabilities. Stay tuned for more updates and new features!
+YData Fabric SDK empowers developers with easy access to state-of-the-art data quality tools and generative AI capabilities. Stay tuned for more updates and new features!
 
 ---
 
 <p align="center">
-  <a href="https://docs.sdk.ydata.ai">Documentation</a>
+  <a href="https://docs.fabric.ydata.ai/latest/sdk/">Documentation</a>
   |
   <a href="https://ydata.ai">More on YData</a>
 </p>
 
 
 ## Overview
 
-The YData SDK is an ecosystem of methods that allows users to, through a python interface, adopt a Data-Centric approach towards the AI development. The solution includes a set of integrated components for data ingestion, standardized data quality evaluation and data improvement, such as synthetic data generation, allowing an iterative improvement of the datasets used in high-impact business applications.
+The Fabric SDK is an ecosystem of methods that allows users to, through a python interface, adopt a Data-Centric approach towards the AI development. The solution includes a set of integrated components for data ingestion, standardized data quality evaluation and data improvement, such as synthetic data generation, allowing an iterative improvement of the datasets used in high-impact business applications.
 
 Synthetic data can be used as Machine Learning performance enhancer, to augment or mitigate the presence of bias in real data. Furthermore, it can be used as a Privacy Enhancing Technology, to enable data-sharing initiatives or even to fuel testing environments.
 
-Under the YData SDK hood, you can find a set of algorithms and metrics based on statistics and deep learning based techniques, that will help you to accelerate your data preparation.
+Under the Fabric SDK hood, you can find a set of algorithms and metrics based on statistics and deep learning based techniques, that will help you to accelerate your data preparation.
 
 ### What you can expect:
 
-YData SDK is composed by the following main modules:
+Fabric SDK is composed by the following main modules:
 
 - **Datasources**
-  - YData’s SDK includes several connectors for easy integration with existing data sources. It supports several storage types, like filesystems and RDBMS. Check the list of connectors.
-  - SDK’s Datasources run on top of Dask, which allows it to deal with not only small workloads but also larger volumes of data.
+  - Fabric’s SDK includes several connectors for easy integration with existing data sources. It supports several storage types, like filesystems and RDBMS. Check the list of connectors.
+  - Fabric SDK’s Datasources run on top of Dask, which allows it to deal with not only small workloads but also larger volumes of data.
 
 - **Synthesizers**
   - Simplified interface to train a generative model and learn in a data-driven manner the behavior, the patterns and original data distribution. Optimize your model for privacy or utility use-cases.