Update README.md

README.md

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-		<div class="underline">Read announcement blog post</div>
+		<div class="underline">Instagram</div>
 	</a>
 	<a href="https://twitter.com/Datatrooper1" class="block overflow-hidden">
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-		<div class="underline">Video Walkthrough with Philipp Schmid</div>
+		<div class="underline">Twitter</div>
 	</a>
 	<a
 		href="https://datatrooper.github.io/intro/"
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-		<div class="underline">Documentation: Hugging Face in SageMaker</div>
+		<div class="underline">Home page</div>
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 		<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.
+			We are just two friends that love Data Science and want to create an impact using AI technologies.
 		</p>
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 		<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/"