diff --git a/blogs.html b/blogs.html
new file mode 100644
index 0000000000..d83859e800
--- /dev/null
+++ b/blogs.html
@@ -0,0 +1,93 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <script async src="https://www.googletagmanager.com/gtag/js?id=UA-156955408-1"></script>
+    <script>
+      window.dataLayer = window.dataLayer || [];
+      function gtag(){dataLayer.push(arguments);}
+      gtag('js', new Date());
+      gtag('config', 'UA-156955408-1');
+    </script>
+
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <title>ONNX Runtime | Community</title>
+    <link rel="icon" href="./images/ONNXRuntime-Favicon.png" type="image/gif" sizes="16x16">
+    <link rel="stylesheet" href="css/fonts.css">
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
+    <link rel="stylesheet" href="css/custom.css">
+    <link rel="stylesheet" href="css/responsive.css">
+</head>
+
+<body>
+    <a class="skip-main" href="#skipMain">Skip to main content</a>
+    <div class="main-wrapper">
+        <div class="top-banner-bg">
+
+            <!-- Partial header.html Start-->
+            <div w3-include-html="header.html"></div>
+            <!-- Partial header.html End-->
+
+            <div id="skipMain" role="main">
+                <div class="outer-container mx-auto py-5">
+                    
+                    <section class="blue-title-columns py-md-5 pb-4 pt-4 mt-5">
+                        <div class="container-fluid">
+                            <h1 class="mb-3 blue-text">Blogs & Announcements</h1>
+                            </div>
+							<div id="blog-list"></div>
+
+                        </div>
+                    </section>
+                    <!--add template here-->
+
+                </div>
+            </div>
+	    </div>	    
+    </div>
+    <!-- Partial footer.html Start-->
+    <div w3-include-html="footer.html"></div>
+    <!-- Partial footer.html End-->
+
+    <a id="back-to-top" href="JavaScript:void(0);" class="btn btn-lg back-to-top" role="button" aria-label="Back to top"><span class="fa fa-angle-up"></span></a>
+
+    <script src="https://www.w3schools.com/lib/w3.js"></script>
+    <script>w3.includeHTML();</script>
+    <script src="https://code.jquery.com/jquery-3.4.1.min.js"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js"></script>
+    <script src="./js/custom.js"></script>
+
+	<!--create a custom web component-->
+	<template id="blog-item">
+		<div class="news row blue-title-columns">
+			<div class="col-12 blue-text">
+				<h3 class="mr-xl-5 mb-3">
+					<slot name="title"></slot>
+				</h3>
+				<span class="article-date">
+					<slot name="date"></slot>
+				</span>
+				<br/>
+				<span class="article-blurb">
+					<slot name="blurb"></slot>
+				</span>
+				<br/>
+				<div >
+					<a href=""  name="link"
+					target="_blank" class="link">
+					<span class="link-content">
+						Read more
+					</span>
+					<span class="link-arrow fa fa-angle-right"></span>
+					</a>
+				</div>
+			</div>
+		</div>
+	</template>
+
+</body>
+
+</html>
diff --git a/footer.html b/footer.html
index 894560012a..e935f30bdd 100644
--- a/footer.html
+++ b/footer.html
@@ -41,8 +41,8 @@
                     <h3 class="footer-heading">Resources</h3>
                     <ul class="list-unstyled text-small">
                         <li class="mb-2">
-                            <a target="_blank" href="./news.html">
-                                <span class="link-content">News</span><span class="link-arrow fa fa-angle-right"></span></a>
+                            <a target="_blank" href="./blogs.html">
+                                <span class="link-content">Blogs</span><span class="link-arrow fa fa-angle-right"></span></a>
                         </li>
                         <li class="mb-2">
                             <a target="_blank" href="https://www.onnxruntime.ai/docs/tutorials/">
diff --git a/header.html b/header.html
index bd4fc69a50..5f44703fe2 100644
--- a/header.html
+++ b/header.html
@@ -17,7 +17,7 @@
                         <a class="nav-link pr-3" href="./docs">Docs</a>
                     </li>
                     <li class="nav-item">
-                        <a class="nav-link pr-3" href="./news.html">News</a>
+                        <a class="nav-link pr-3" href="./blogs.html">Blogs</a>
                     </li>
                     <li class="nav-item">
                         <a class="nav-link pr-3" href="./about.html">About</a>
@@ -29,7 +29,6 @@
                             <a class="dropdown-item" href="https://www.youtube.com/onnxruntime" target="_blank">YouTube</a>
                             <a class="dropdown-item" href="https://www.linkedin.com/company/77691267/admin/" target="_blank">Linkedin</a>
                             <a class="dropdown-item" href="https://www.tiktok.com/@onnxruntimeai" target="_blank">TikTok</a>
-                            <a class="dropdown-item" href="https://twitter.com/onnxruntime" target="_blank">Twitter</a>
                         </div>
                     </li>
                     <li class="nav-item">
diff --git a/images/BERT-ORTTRT.png b/images/BERT-ORTTRT.png
deleted file mode 100644
index 21e1fb5e97..0000000000
Binary files a/images/BERT-ORTTRT.png and /dev/null differ
diff --git a/images/gptc-vscode.png b/images/gptc-vscode.png
deleted file mode 100644
index e70679fb42..0000000000
Binary files a/images/gptc-vscode.png and /dev/null differ
diff --git a/images/hugging-optimum-ort-training.png b/images/hugging-optimum-ort-training.png
deleted file mode 100644
index ef94ea1dbc..0000000000
Binary files a/images/hugging-optimum-ort-training.png and /dev/null differ
diff --git a/images/huggingface-spaces.png b/images/huggingface-spaces.png
deleted file mode 100644
index 544d7598f0..0000000000
Binary files a/images/huggingface-spaces.png and /dev/null differ
diff --git a/images/hypefactors-ort.png b/images/hypefactors-ort.png
deleted file mode 100644
index 5ba4163986..0000000000
Binary files a/images/hypefactors-ort.png and /dev/null differ
diff --git a/images/ort-pt-training.png b/images/ort-pt-training.png
deleted file mode 100644
index e61ef23e67..0000000000
Binary files a/images/ort-pt-training.png and /dev/null differ
diff --git a/images/ort-rocm.png b/images/ort-rocm.png
deleted file mode 100644
index 520767a17e..0000000000
Binary files a/images/ort-rocm.png and /dev/null differ
diff --git a/images/ort-training-code.png b/images/ort-training-code.png
deleted file mode 100644
index 7079e1336c..0000000000
Binary files a/images/ort-training-code.png and /dev/null differ
diff --git a/images/ort-web.png b/images/ort-web.png
deleted file mode 100644
index d8a542bde6..0000000000
Binary files a/images/ort-web.png and /dev/null differ
diff --git a/images/ort_intel.png b/images/ort_intel.png
deleted file mode 100644
index b55c84d021..0000000000
Binary files a/images/ort_intel.png and /dev/null differ
diff --git a/images/ovepblog.png b/images/ovepblog.png
deleted file mode 100644
index ce83bc21b5..0000000000
Binary files a/images/ovepblog.png and /dev/null differ
diff --git a/images/sas-ort.png b/images/sas-ort.png
deleted file mode 100644
index dd6096a487..0000000000
Binary files a/images/sas-ort.png and /dev/null differ
diff --git a/images/xamarin-ort.png b/images/xamarin-ort.png
deleted file mode 100644
index 22286e80a7..0000000000
Binary files a/images/xamarin-ort.png and /dev/null differ
diff --git a/js/blogs.json b/js/blogs.json
new file mode 100644
index 0000000000..b8e7abc2e9
--- /dev/null
+++ b/js/blogs.json
@@ -0,0 +1,106 @@
+{
+    "blogs": [
+        {
+            "title": "Azure Container for PyTorch is now Generally Available in Azure Machine Learning!",
+            "date": "March 22nd, 2023",
+            "blurb": "ACPT provides a ready-to-use distributed training environment for users to run on the latest multi-node GPU infrastructure offered in Azure. With Nebula, a new fast checkpointing capability in ACPT, you can save your checkpoints 1000 times faster with a simple API that works asynchronously with your training process.",
+            "link": "https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/azure-container-for-pytorch-is-now-generally-available-in-azure/ba-p/3774616"
+        },
+        {
+            "title": "High-performance deep learning in Oracle Cloud with ONNX Runtime",
+            "date": "March 15th, 2023",
+            "blurb": "Enabling scenarios through the usage of Deep Neural Network (DNN) models is critical to our AI strategy at Oracle, and our Cloud AI Services team has built a solution to serve DNN models for customers in the healthcare sector. In this blog post, we’ll share challenges our team faced, and how ONNX Runtime solves these as the backbone of success for high-performance inferencing.",
+            "link": "https://cloudblogs.microsoft.com/opensource/2023/03/15/high-performance-deep-learning-in-oracle-cloud-with-onnx-runtime/"
+        },
+        {
+            "title": "Inference Stable Diffusion with C# and ONNX Runtime",
+            "date": "March 9th, 2023",
+            "blurb": "In this tutorial we will learn how to do inferencing for the popular Stable Diffusion deep learning model in C#. Stable Diffusion models take a text prompt and create an image that represents the text. ",
+            "link": "https://onnxruntime.ai/docs/tutorials/csharp/stable-diffusion-csharp.html"
+        },
+        {
+            "title": "Video super resolution in Microsoft Edge",
+            "date": "March 8th, 2023",
+            "blurb": "VSR in Microsoft Edge builds on top of ONNX Runtime and DirectML making our solution portable across GPU vendors and allowing VSR to be available to more users. Additional graphics cards which support these technologies and have sufficient computing power will receive support in the future. The ONNX Runtime and DirectML teams have fine-tuned their technology over many years, resulting in VSR making the most of the performance and capabilities of your graphics card’s processing power.",
+            "link": "https://blogs.windows.com/msedgedev/2023/03/08/video-super-resolution-in-microsoft-edge/"
+        },
+        {
+            "title": "OctoML drives down production AI inference costs at Microsoft through new integration with ONNX Runtime ecosystem",
+            "date": "March 2nd, 2023",
+            "blurb": "Over the past year, OctoML engineers worked closely with Watch For to design and implement the TVM Execution Provider (EP) for ONNX Runtime - bringing the model optimization potential of Apache TVM to all ONNX Runtime users. This builds upon the collaboration we began in 2021, to bring the benefits of TVM’s code generation and flexible quantization support to production scale at Microsoft.",
+            "link": "https://octoml.ai/blog/octoml-drives-down-costs-at-microsoft-through-new-integration-with-onnx-runtime/"
+        },
+        {
+            "title": "Performant on-device inferencing with ONNX Runtime",
+            "date": "February 8th, 2023",
+            "blurb": "On-device machine learning model serving is a difficult task, especially given the limited bandwidth of early-stage startups. This guest post from the team at Pieces shares the problems and solutions evaluated for their on-device model serving stack and how ONNX Runtime serves as their backbone of success.",
+            "link": "https://cloudblogs.microsoft.com/opensource/2023/02/08/performant-on-device-inferencing-with-onnx-runtime/"
+        },
+    {
+        "title": "Improve BERT inference speed by combining the power of Optimum, OpenVINO™, ONNX Runtime, and Azure",
+        "date": "January 25th, 2023",
+        "blurb": "In this blog, we will discuss one of the ways to make huge models like BERT smaller and faster with OpenVINO™ Neural Networks Compression Framework (NNCF) and ONNX Runtime with OpenVINO™ Execution Provider through Azure Machine Learning.",
+		"link": "https://cloudblogs.microsoft.com/opensource/2023/01/25/improve-bert-inference-speed-by-combining-the-power-of-optimum-openvino-onnx-runtime-and-azure/"
+    },
+    {
+        "title": "Optimum + ONNX Runtime: Easier, Faster training for your Hugging Face models",
+        "date": "January 24th, 2023",
+        "blurb": "Hugging Face’s Optimum library, through its integration with ONNX Runtime for training, provides an open solution to improve training times by 35% or more for many popular Hugging Face models. We present details of both Hugging Face Optimum and the ONNX Runtime Training ecosystem, with performance numbers highlighting the benefits of using the Optimum library.",
+        "link": "https://huggingface.co/blog/optimum-onnxruntime-training/"
+    },
+    {
+        "title": "Live demos of machine learning models with ONNX and Hugging Face Spaces",
+        "date": "June 6, 2022",
+        "blurb": "Choosing which machine learning model to use, sharing a model with a colleague, and quickly trying out a model are all reasons why you may find yourself wanting to quickly run inference on a model. You can configure your environment and download Jupyter notebooks, but it would be nicer if there was a way to run a model with even less effort...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2022/06/06/live-demos-of-machine-learning-models-with-onnx-and-hugging-face-spaces/"
+    },
+    {
+        "title": "Optimizing and deploying transformer INT8 inference with ONNX Runtime-TensorRT on NVIDIA GPUs",
+        "date": "May 2, 2022",
+        "blurb": "Transformer-based models have revolutionized the natural language processing (NLP) domain. Ever since its inception, transformer architecture has been integrated into models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) for performing tasks such as text generation or summarization and question and answering to name a few...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2022/05/02/optimizing-and-deploying-transformer-int8-inference-with-onnx-runtime-tensorrt-on-nvidia-gpus/"
+    },
+    {
+        "title": "Scaling-up PyTorch inference: Serving billions of daily NLP inferences with ONNX Runtime",
+        "date": "April 19, 2022",
+        "blurb": "Scale, performance, and efficient deployment of state-of-the-art Deep Learning models are ubiquitous challenges as applied machine learning grows across the industry. We’re happy to see that the ONNX Runtime Machine Learning model inferencing solution we’ve built and use in high-volume Microsoft products and services also resonates with our open source community, enabling new capabilities that drive content relevance and productivity...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2022/04/19/scaling-up-pytorch-inference-serving-billions-of-daily-nlp-inferences-with-onnx-runtime/"
+    },
+    {
+        "title": "Add AI to mobile applications with Xamarin and ONNX Runtime",
+        "date": "December 14, 2021",
+        "blurb": "ONNX Runtime now supports building mobile applications in C# with Xamarin. Support for Android and iOS is included in the ONNX Runtime release 1.10 NuGet package. This enables C# developers to build AI applications for Android and iOS to execute ONNX models on mobile devices with ONNX Runtime...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2021/12/14/add-ai-to-mobile-applications-with-xamarin-and-onnx-runtime/"
+    },
+    {
+        "title": "ONNX Runtime Web—running your machine learning model in browser",
+        "date": "September 2, 2021",
+        "blurb": "We are introducing ONNX Runtime Web (ORT Web), a new feature in ONNX Runtime to enable JavaScript developers to run and deploy machine learning models in browsers. It also helps enable new classes of on-device computation. ORT Web will be replacing the soon to be deprecated onnx.js...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2021/09/02/onnx-runtime-web-running-your-machine-learning-model-in-browser/"
+    },
+    {
+        "title": "Accelerate PyTorch transformer model training with ONNX Runtime – a deep dive",
+        "date": "July 13, 2021",
+        "blurb": "ONNX Runtime (ORT) for PyTorch accelerates training large scale models across multiple GPUs with up to 37% increase in training throughput over PyTorch and up to 86% speed up when combined with DeepSpeed...",
+        "link": "https://techcommunity.microsoft.com/t5/azure-ai/accelerate-pytorch-transformer-model-training-with-onnx-runtime/ba-p/2540471"
+    },
+    {
+        "title": "Accelerate PyTorch training with torch-ort",
+        "date": "July 13, 2021",
+        "blurb": "With a simple change to your PyTorch training script, you can now speed up training large language models with torch_ort.ORTModule, running on the target hardware of your choice. Training deep learning models requires ever-increasing compute and memory resources. Today we release torch_ort.ORTModule, to accelerate distributed training of PyTorch models, reducing the time and resources needed for training...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2021/07/13/accelerate-pytorch-training-with-torch-ort/"
+    },
+    {
+        "title": "ONNX Runtime release 1.8.1 previews support for accelerated training on AMD GPUs with the AMD ROCm™ Open Software Platform",
+        "date": "July 13, 2021",
+        "blurb": "ONNX Runtime is an open-source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. Today, we are excited to announce a preview version of ONNX Runtime in release 1.8.1 featuring support for AMD Instinct™ GPUs facilitated by the AMD ROCm™ open software platform...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2021/07/13/onnx-runtime-release-1-8-1-previews-support-for-accelerated-training-on-amd-gpus-with-the-amd-rocm-open-software-platform/"
+    },
+    {
+        "title": "Journey to optimize large scale transformer model inference with ONNX Runtime",
+        "date": "June 30, 2021",
+        "blurb": "Large-scale transformer models, such as GPT-2 and GPT-3, are among the most useful self-supervised transformer language models for natural language processing tasks such as language translation, question answering, passage summarization, text generation, and so on...",
+        "link": "https://cloudblogs.microsoft.com/opensource/2021/06/30/journey-to-optimize-large-scale-transformer-model-inference-with-onnx-runtime/"
+    }
+]
+}
\ No newline at end of file
diff --git a/js/custom.js b/js/custom.js
index 480e02f500..8a6068b26e 100644
--- a/js/custom.js
+++ b/js/custom.js
@@ -70,6 +70,40 @@ $(document).ready(function () {
   carouselNormalization();
 
   $('.nav-tabs').responsiveTabs();
+
+  //populate blogs
+ $.getJSON("js/blogs.json", function (json) {
+
+    var template = document.querySelector('#blog-item');
+    var blogList = document.querySelector('#blog-list');
+
+    json.blogs.forEach(function(blog) {
+      var clone = template.content.cloneNode(true);
+      clone.querySelector('[name="title"]').textContent = blog.title;
+      clone.querySelector('[name="date"]').textContent = blog.date;
+      clone.querySelector('[name="blurb"]').textContent = blog.blurb;
+      clone.querySelector('[name="link"]').href = blog.link;
+      blogList.appendChild(clone);
+    });
+
+  });
+
+//   //create local json and template fill for testing
+//   var template = document.querySelector('#blog-item');
+//   var blogList = document.querySelector('#blog-list');
+//   var blog = {
+//     title: "Test Title",
+//     date: "Test Date",
+//     blurb: "Test Blurb",
+//     link: "https://cloudblogs.microsoft.com/opensource/2023/01/25/improve-bert-inference-speed-by-combining-the-power-of-optimum-openvino-onnx-runtime-and-azure/"
+//   };
+//   var clone = template.content.cloneNode(true);
+//   clone.querySelector('[name="title"]').textContent = blog.title;
+//   clone.querySelector('[name="date"]').textContent = blog.date;
+//   clone.querySelector('[name="blurb"]').textContent = blog.blurb;
+//   clone.querySelector('[name="link"]').href = blog.link;
+//   blogList.appendChild(clone);
+   
 });
 
 function getStartedScroll() {
diff --git a/news.html b/news.html
index 576b145d7a..3c376ae57b 100644
--- a/news.html
+++ b/news.html
@@ -12,6 +12,7 @@
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+
     <title>ONNX Runtime | Community</title>
     <link rel="icon" href="./images/ONNXRuntime-Favicon.png" type="image/gif" sizes="16x16">
     <link rel="stylesheet" href="css/fonts.css">
@@ -19,222 +20,16 @@
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
     <link rel="stylesheet" href="css/custom.css">
     <link rel="stylesheet" href="css/responsive.css">
+	<script type="text/JavaScript">
+		var root = window.location.protocol + "//" + window.location.hostname;
+		window.location.replace(root + "/blogs.html");
+		
+	</script>
 </head>
-
 <body>
-    <a class="skip-main" href="#skipMain">Skip to main content</a>
-    <div class="main-wrapper">
-        <div class="top-banner-bg">
 
-            <!-- Partial header.html Start-->
-            <div w3-include-html="header.html"></div>
-            <!-- Partial header.html End-->
+    <p><a href="/blogs.html">This page has moved to Blogs</a></p>
 
-            <div id="skipMain" role="main">
-                <div class="outer-container mx-auto py-5">
-                    
-                    <section class="blue-title-columns py-md-5 pb-4 pt-4 mt-5">
-                        <div class="container-fluid">
-                            <h1 class="mb-3 blue-text">News & Announcements</h1>
-                            </div>
-							<div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Improve BERT inference speed by combining the power of Optimum, OpenVINO™, ONNX Runtime, and Azure</h3>
-                                    <span class="article-date">January 25th, 2023</span><br/><br/>
-									<span class="article-blurb">
-                                        In this blog, we will discuss one of the ways to make huge models like BERT smaller and faster with OpenVINO™ Neural Networks Compression Framework (NNCF) and ONNX Runtime with OpenVINO™ Execution Provider through Azure Machine Learning.
-                                    </span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2023/01/25/improve-bert-inference-speed-by-combining-the-power-of-optimum-openvino-onnx-runtime-and-azure/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ovepblog.png" alt="Code blurb from tutorial"></div>
-								</div>
-							</div>
-							<div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Optimum + ONNX Runtime: Easier, Faster training for your Hugging Face models</h3>
-                                    <span class="article-date">January 24th, 2023</span><br/><br/>
-									<span class="article-blurb">
-                                        Hugging Face’s Optimum library, through its integration with ONNX Runtime for training, provides an open solution to improve training times by 35% or more for many popular Hugging Face models. We present details of both Hugging Face Optimum and the ONNX Runtime Training ecosystem, with performance numbers highlighting the benefits of using the Optimum library.
-                                    </span><br/><br/><a href="https://huggingface.co/blog/optimum-onnxruntime-training/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/hugging-optimum-ort-training.png" alt="Code blurb from tutorial"></div>
-								</div>
-							</div>
-
-                            <div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Live demos of machine learning models with ONNX and Hugging Face Spaces</h3>
-                                    <span class="article-date">June 6, 2022</span><br/><br/>
-									<span class="article-blurb">
-                                        Choosing which machine learning model to use, sharing a model with a colleague, and quickly trying out a model are all reasons why you may find yourself wanting to quickly run inference on a model. You can configure your environment and download Jupyter notebooks, but it would be nicer if there was a way to run a model with even less effort...
-                                    </span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2022/06/06/live-demos-of-machine-learning-models-with-onnx-and-hugging-face-spaces/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/huggingface-spaces.png" alt="Code blurb from tutorial"></div>
-								</div>
-							</div>
-                            
-                            <div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Optimizing and deploying transformer INT8 inference with ONNX Runtime-TensorRT on NVIDIA GPUs</h3>
-                                    <span class="article-date">May 2, 2022</span><br/><br/>
-									<span class="article-blurb">
-                                        Transformer-based models have revolutionized the natural language processing (NLP) domain. Ever since its inception, transformer architecture has been integrated into models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) for performing tasks such as text generation or summarization and question and answering to name a few...
-                                    </span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2022/05/02/optimizing-and-deploying-transformer-int8-inference-with-onnx-runtime-tensorrt-on-nvidia-gpus/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/BERT-ORTTRT.png" alt="Graph comparing BERT model with ORT-TRT and PyTorch"></div>
-								</div>
-							</div>
-
-                            <div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Scaling-up PyTorch inference: Serving billions of daily NLP inferences with ONNX Runtime</h3>
-                                    <span class="article-date">April 19, 2022</span><br/><br/>
-									<span class="article-blurb">
-                                        Scale, performance, and efficient deployment of state-of-the-art Deep Learning models are ubiquitous challenges as applied machine learning grows across the industry. We’re happy to see that the ONNX Runtime Machine Learning model inferencing solution we’ve built and use in high-volume Microsoft products and services also resonates with our open source community, enabling new capabilities that drive content relevance and productivity...
-                                    </span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2022/04/19/scaling-up-pytorch-inference-serving-billions-of-daily-nlp-inferences-with-onnx-runtime/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/hypefactors-ort.png" alt="Hypefactors and ORT image"></div>
-								</div>
-							</div>
-                            
-                            <div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Add AI to mobile applications with Xamarin and ONNX Runtime</h3>
-                                    <span class="article-date">December 14, 2021</span><br/><br/>
-									<span class="article-blurb">
-                                        ONNX Runtime now supports building mobile applications in C# with Xamarin. Support for Android and iOS is included in the ONNX Runtime release 1.10 NuGet package. This enables C# developers to build AI applications for Android and iOS to execute ONNX models on mobile devices with ONNX Runtime...
-                                    </span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/12/14/add-ai-to-mobile-applications-with-xamarin-and-onnx-runtime/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/xamarin-ort.png" alt="Xamarin and ORT image"></div>
-								</div>
-							</div>
-
-                            <div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">ONNX Runtime Web—running your machine learning model in browser</h3>
-                                    <span class="article-date">September 2, 2021</span><br/><br/>
-									<span class="article-blurb">
-										We are introducing ONNX Runtime Web (ORT Web), a new feature in ONNX Runtime to enable JavaScript developers to run and deploy machine learning models in browsers. It also helps enable new classes of on-device computation. ORT Web will be replacing the soon to be deprecated onnx.js...
-									</span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/09/02/onnx-runtime-web-running-your-machine-learning-model-in-browser/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort-web.png" alt="ORT Web overview diagram"></div>
-								</div>
-							</div>
-
-                            <div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Accelerate PyTorch transformer model training with ONNX Runtime – a deep dive</h3>
-                                    <span class="article-date">July 13, 2021</span><br/><br/>
-									<span class="article-blurb">
-										ONNX Runtime (ORT) for PyTorch accelerates training large scale models across multiple GPUs with up to 37% increase in training throughput over PyTorch and up to 86% speed up when combined with DeepSpeed...
-									</span><br/><br/><a href="https://techcommunity.microsoft.com/t5/azure-ai/accelerate-pytorch-transformer-model-training-with-onnx-runtime/ba-p/2540471" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort-pt-training.png" alt="ORT Training performance"></div>
-								</div>
-							</div>
-
-							<div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Accelerate PyTorch training with torch-ort</h3>
-                                    <span class="article-date">July 13, 2021</span><br/><br/>
-									<span class="article-blurb">
-										With a simple change to your PyTorch training script, you can now speed up training large language models with torch_ort.ORTModule, running on the target hardware of your choice.<br/><br/><p>Training deep learning models requires ever-increasing compute and memory resources. Today we release torch_ort.ORTModule, to accelerate distributed training of PyTorch models, reducing the time and resources needed for training...</p>
-									</span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/07/13/accelerate-pytorch-training-with-torch-ort/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort-training-code.png" alt="Simple code for ORT Training"></div>
-								</div>
-							</div>
-
-                            <div class="news row blue-title-columns ">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">ONNX Runtime release 1.8.1 previews support for accelerated training on AMD GPUs with the AMD ROCm™ Open Software Platform</h3>
-                                    <span class="article-date">July 13, 2021</span><br/><br/>
-                                    <span class="article-blurb">
-										ONNX Runtime is an open-source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. Today, we are excited to announce a preview version of ONNX Runtime in release 1.8.1 featuring support for AMD Instinct™ GPUs facilitated by the AMD ROCm™ open software platform...</span>
-										<br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/07/13/onnx-runtime-release-1-8-1-previews-support-for-accelerated-training-on-amd-gpus-with-the-amd-rocm-open-software-platform/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort-rocm.png" alt="ORT and ROCm"></div>
-								</div>
-							</div>
-
-							<div class="news row blue-title-columns ">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Journey to optimize large scale transformer model inference with ONNX Runtime</h3>
-                                    <span class="article-date">June 30, 2021</span><br/><br/>
-									<span class="article-blurb">
-										Large-scale transformer models, such as GPT-2 and GPT-3, are among the most useful self-supervised transformer language models for natural language processing tasks such as language translation, question answering, passage summarization, text generation, and so on...</span><br/>
-										<br/><a href="https://cloudblogs.microsoft.com/opensource/2021/06/30/journey-to-optimize-large-scale-transformer-model-inference-with-onnx-runtime/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/gptc-vscode.png" alt="Deploying GTP-C in VS Code"></div>
-								</div>
-							</div>
-
-							<div class="news row blue-title-columns ">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">SAS and Microsoft collaborate to democratize the use of Deep Learning Models</h3>
-									<span class="article-date">March 30, 2021</span><br/><br/>
-                                    <span class="article-blurb">
-									Artificial Intelligence (AI) developers enjoy the flexibility of choosing a model training framework of their choice. This includes both open-source frameworks as well as vendor-specific ones. While this is great for innovation, it does introduce the challenge of operationalization across different hardware platforms...</span>
-									<br/><br/><a href="https://communities.sas.com/t5/SAS-Communities-Library/SAS-and-Microsoft-collaborate-to-democratize-the-use-of-Deep/ta-p/730072" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/sas-ort.png" alt="ORT and SAS"></div>
-								</div>
-							
-							</div>
-
-							<div class="news row blue-title-columns">
-								<div class="col-12 col-md-8 mb-4 mb-md-0 blue-text">
-									<h3 class="mr-xl-5 mb-3">Optimizing BERT model for Intel CPU Cores using ONNX runtime default execution provider</h3>
-									<span class="article-date">March 1, 2021</span><br/><br/>
-                                    <span class="article-blurb">
-									The performance improvements provided by ONNX Runtime powered by Intel® Deep Learning Boost: Vector Neural Network Instructions (Intel® DL Boost: VNNI) greatly improves performance of machine learning model execution for developers...
-									</span><br/><br/><a href="https://cloudblogs.microsoft.com/opensource/2021/03/01/optimizing-bert-model-for-intel-cpu-cores-using-onnx-runtime-default-execution-provider/" target="_blank" class="link"><span class="link-content">Read
-										more</span><span class="link-arrow fa fa-angle-right"></span></a>
-								</div>
-
-								<div class="col-12 col-md-4 mb-4 mb-md-0 text-center">
-									<div class="news-img pb-4 pb-md-5 mr-xl-5 text-center"><img src="./images/ort_intel.png" alt="ORT and Intel AI"></div>
-								</div>
-							</div>
-
-							
-                        </div>
-                    </section>
-                </div>
-            </div>
-	    </div>	    
-    </div>
     <!-- Partial footer.html Start-->
     <div w3-include-html="footer.html"></div>
     <!-- Partial footer.html End-->
