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README.md

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 title: README
-emoji: 🐢
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+emoji: 🪖
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+colorTo: black
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-Edit this `README.md` markdown file to author your organization card 🔥
+<div class="grid lg:grid-cols-3 gap-x-4 gap-y-7">
+	<p class="lg:col-span-3">
+		Hugging Face is working with Amazon Web Services to make it easier than
+		ever for startups and enterprises to <strong
+			>train and deploy Hugging Face models in Amazon SageMaker</strong
+		>.
+	</p>
+	<a
+		href="https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"
+		class="block overflow-hidden group"
+	>
+		<div
+			class="w-full h-40 object-cover mb-2 bg-indigo-100 rounded-lg flex items-center justify-center dark:bg-gray-900 dark:group-hover:bg-gray-850"
+		>
+			<img
+				alt=""
+				src="/front/assets/promo/amazon_sagemaker_x_huggingface.png"
+				class="w-40"
+			/>
+		</div>
+		<div class="underline">Read announcement blog post</div>
+	</a>
+	<a href="https://youtu.be/ok3hetb42gU" class="block overflow-hidden">
+		<img
+			alt=""
+			src="/front/assets/promo/amazon_walkthrough_thumbnail.png"
+			class="w-full h-40 object-cover mb-2 bg-gray-300 rounded-lg"
+		/>
+		<div class="underline">Video Walkthrough with Philipp Schmid</div>
+	</a>
+	<a
+		href="https://huggingface.co/docs/sagemaker"
+		class="block overflow-hidden group"
+	>
+		<div
+			class="w-full h-40 object-cover mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start"
+		>
+			<img
+				alt=""
+				src="/front/assets/promo/amazon_documentation.png"
+				class="w-44 p-4"
+			/>
+		</div>
+		<div class="underline">Documentation: Hugging Face in SageMaker</div>
+	</a>
+	<div class="lg:col-span-3">
+		<p class="mb-2">
+			To train Hugging Face models in Amazon SageMaker, you can use the
+			Hugging Face Deep Learning Contrainers (DLCs) and the Hugging Face
+			support in the SageMaker Python SDK.
+		</p>
+		<p class="mb-2">
+			The DLCs are fully integrated with the SageMaker distributed training
+			libraries to train models more quickly using the latest generation of
+			accelerated computing instances available on Amazon EC2. With the
+			SageMaker Python SDK, you can start training with just a single line of
+			code, enabling your teams to move from idea to production more quickly.
+		</p>
+		<p class="mb-2">
+			To deploy Hugging Face models in Amazon SageMaker, you can use the
+			Hugging Face Deep Learning Containers with the new Hugging Face
+			Inference Toolkit.
+		</p>
+		<p class="mb-2">
+			With the new Hugging Face Inference DLCs, deploy your trained models for
+			inference with just one more line of code, or select any of the 10,000+
+			models publicly available on the 🤗 Hub, and deploy them with Amazon
+			SageMaker, to easily create production-ready endpoints that scale
+			seamlessly, with built-in monitoring and enterprise-level security.
+		</p>
+		<p>
+			More information: <a
+				href="https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-simplify-and-accelerate-adoption-of-natural-language-processing-models/"
+				class="underline">AWS blog post</a
+			>,
+			<a
+				href="https://discuss.huggingface.co/c/sagemaker/17"
+				class="underline">Community Forum</a
+			>
+		</p>
+	</div>
+</div>