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README.md

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 ## Build and Scale AI/ML on AWS
 
-AWS offers a comprehensive suite of AI/ML tools and services that cater to every stage of the machine learning lifecycle. From model development and training to deployment and inference, AWS provides cutting-edge solutions like Amazon SageMaker with its JumpStart feature for end-to-end development and deployment of models, Amazon Bedrock for building generative AI applications, custom AI accelerator chips such as AWS Trainium for training and AWS Inferentia for inference, and pre-configured environments to streamline your ML workflows. Additionally, you can explore the Registry of Open Data to discover, access, and utilize diverse datasets for your AI/ML projects. Whether you're working on large language models, generative AI, computer vision, time-series forecasting, or natural language processing, scaling your projects on AWS is easy.
+AWS offers a comprehensive suite of AI/ML tools and services that cater to every stage of the machine learning lifecycle. From model development and training to deployment and inference, AWS provides cutting-edge solutions such Amazon SageMaker as a fully-managed service for end-to-end development and deployment of models, Amazon Bedrock for building generative AI applications, custom AI accelerator chips such as AWS Trainium for training and AWS Inferentia for inference, and pre-configured environments to streamline your ML workflows. Additionally, you can explore the Registry of Open Data to discover, access, and utilize diverse datasets for your AI/ML projects. Whether you're working on large language models, generative AI, computer vision, time-series forecasting, or natural language processing, scaling your projects on AWS is easy.
 
 Learn more about these services and others:
 
@@ -50,7 +50,7 @@ Learn more about these services and others:
 
 ## AWS & Hugging Face Collaboration
 
-AWS and Hugging Face are working together to simplify and accelerate the adoption of advanced machine learning models. This [partnership](https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-make-generative-ai-more-accessible-and-cost-efficient/?trk=7902b1b7-22c0-4841-8f9d-50fa299e5e8a&sc_channel=el) offers streamlined training using Hugging Face Deep Learning Containers with SageMaker distributed training libraries, simplifying workflows with the SageMaker Python SDK for efficient model training. Deployment is made effortless through the Hugging Face Inference toolkit and DLCs, allowing users to deploy trained models on the Hugging Face Hub. Amazon SageMaker facilitates the creation of scalable endpoints with built-in monitoring and enterprise-level security. This joint effort empowers teams to move quickly from experimentation to production, leveraging cutting-edge models and scalable infrastructure to drive innovation in machine learning projects.
+AWS and Hugging Face are working together to simplify and accelerate the adoption of advanced machine learning models. This [collaboration](https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-make-generative-ai-more-accessible-and-cost-efficient/?trk=7902b1b7-22c0-4841-8f9d-50fa299e5e8a&sc_channel=el) offers streamlined training using Hugging Face Deep Learning Containers with SageMaker distributed training libraries, simplifying workflows with the SageMaker Python SDK for efficient model training. Deployment is made effortless through the Hugging Face Inference toolkit and DLCs, allowing users to deploy trained models on the Hugging Face Hub. Amazon SageMaker facilitates the creation of scalable endpoints with built-in monitoring and enterprise-level security. This joint effort empowers teams to move quickly from experimentation to production, leveraging cutting-edge models and scalable infrastructure to drive innovation in machine learning projects.
 
 * Learn about [Hugging Face on AWS](https://aws.amazon.com/ai/hugging-face/?trk=3983b951-6548-4c2c-bd3c-c0429efec685&sc_channel=el)
 * Learn about [Hugging Face in Amazon SageMaker](https://huggingface.co/docs/sagemaker/index)