Update README.md

Author: gsheni

README.md

@@ -3,7 +3,20 @@
 <p align="center">
     <i>This repository is part of <a href="https://sdv.dev">The Synthetic Data Vault Project</a>, a project from <a href="https://datacebo.com">DataCebo</a>.</i>
 </p>
-<div align="left">
+
+[![Dev Status](https://img.shields.io/badge/Dev%20Status-5%20--%20Production%2fStable-green)](https://pypi.org/search/?c=Development+Status+%3A%3A+5+-+Production%2FStable)
+[![Unit Tests](https://github.com/datacebo/PyMetrics/actions/workflows/unit.yaml/badge.svg)](https://github.com/datacebo/PyMetrics/actions/workflows/unit.yaml)
+[![Slack](https://img.shields.io/badge/Slack-Join%20now!-36C5F0?logo=slack)](https://bit.ly/sdv-slack-invite)
+
+<div align="center">
+  <a href="https://datacebo.com">
+  <picture>
+      <source media="(prefers-color-scheme: dark)" srcset="https://github.com/sdv-dev/SDV/blob/stable/docs/images/datacebo-logo-dark-mode.png">
+      <img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/stable/docs/images/datacebo-logo.png"></img>
+  </picture></a>
+</div>
+<br/>
+</div>
 
 # PyMetrics
 The PyMetrics project allows you to extract download metrics for Python libraries published on [PyPI](https://pypi.org/) and [Anaconda](https://www.anaconda.com/).
@@ -69,14 +82,16 @@ This methodology prevents double-counting downloads while providing an accurate
 
 ---
 
-<div align="center">
-  <a href="https://datacebo.com"><picture>
-      <source media="(prefers-color-scheme: dark)" srcset="https://github.com/sdv-dev/SDV/blob/stable/docs/images/datacebo-logo-dark-mode.png">
-      <img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/stable/docs/images/datacebo-logo.png"></img>
-  </picture></a>
-</div>
-<br/>
+<div align="left">
 <br/>
+<p align="center">
+<a href="https://github.com/sdv-dev/SDV">
+<img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/stable/docs/images/SDV-logo.png"></img>
+</a>
+</p>
+</div>
+
+</div>
 
 [The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
 https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we