Merge pull request #166 from scikit-learn/nvidia

Add a blog post announcing NVIDIA sponsorship

_posts/2023-07-11-nvidia-is-a-new-sponsor.md

@@ -0,0 +1,46 @@
+---
+title: "NVIDIA Is A New Sponsor Of The Scikit-Learn consortium at the Inria Foundation"
+date: July 11, 2023
+
+categories:
+  - Funding
+tags:
+  - Sponsor
+
+featured-image: NVIDIAxsklearn.jpg
+
+postauthors:
+  - name: NVIDIA
+    website: https://developer.nvidia.com/gpu-accelerated-libraries
+    image: "nvidia-logo.png"
+  - name: François Goupil
+    email: [email protected]
+    website: https://github.com/francoisgoupil
+    image: "francois_goupil.jpeg"
+---
+<div>
+  <img src="/assets/images/posts_images/{{ page.featured-image }}" alt="">
+  {% include postauthor.html %}
+</div>
+
+<span style="color:red">*Sponsored blog post* </span>
+
+We are thrilled to announce that [NVIDIA](https://www.nvidia.com) has joined the [scikit-learn consortium](https://scikit-learn.fondation-inria.fr/) as a corporate partner. As a leading provider of GPU-accelerated computing solutions, we at NVIDIA recognize the importance of machine learning and the role it plays in the growth of many industries and areas of science. Our partnership with the scikit-learn consortium demonstrates our commitment to supporting the development and advancement of open-source software in the machine learning community.
+
+<div>
+  <video   preload="auto" autoplay loop muted="muted" volume="0"> 
+  <source src="/assets/videos/NVIDIAxsklearn.mp4" type="video/mp4"> 
+  </video>
+</div>
+
+[Scikit-learn](https://scikit-learn.org/stable/) is a popular open-source Python library for machine learning. One of the strengths of scikit-learn is its ease of use and well-defined API. This makes it a favorite tool among data scientists and machine learning practitioners. Thanks to its active community and continuous development, scikit-learn is constantly evolving and improving.
+
+At NVIDIA, we believe that investing in open-source projects like scikit-learn is important. Afterall, it is a central component of the modern data stack in both science and industry. By financially supporting the scikit-learn consortium, we are contributing to the long-term sustainability of scikit-learn and helping to ensure that it remains an easy to use, reliable and valuable tool for years to come. Furthermore, we hope to help advance the project's development, improve its performance, and enhance its capabilities for machine learning on GPUs.
+
+Our partnership with the scikit-learn consortium will also enable us to collaborate more closely with the scikit-learn community, and provide us with insights into how we can improve NVIDIA’s [RAPIDS open-source libraries](https://developer.nvidia.com/rapids) to better serve their needs. We are committed to working with the foundation to ensure that scikit-learn remains a powerful and easy to use machine learning library that meets the needs of data science practitioners in science and industry.
+
+NVIDIA’s commitment to scikit-learn goes beyond financial support. We have hired [Tim Head](https://betatim.github.io), an experienced open-source maintainer, to work full-time on the project. This is not Tim’s first open-source rodeo. He has previously contributed to several high-profile open-source projects, including Project Jupyter. His focus will be reviewing pull requests and coordinating the development of large features. Tim was recently elected as a core maintainer of scikit-learn. His expertise and experience will be invaluable in ensuring the continued growth and success of the project.
+
+In summary, NVIDIA’s partnership with the scikit-learn consortium is an important step in our ongoing commitment to support the development and growth of open-source software in the machine learning community. We are excited to work with the foundation and the community of contributors to help advance the capabilities of scikit-learn and accelerate the development of machine learning applications.
+
+AI helped write this blog post!