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@@ -21,7 +21,7 @@ One stop shop for running AI/ML on AWS.
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- Seamlessly integrate AWS Deep Learning Containers with Amazon SageMaker's managed MLflow service to streamline your ML experiment tracking, model management, and deployment workflow. Checkout [Level Up with SageMaker AI & MLflow](https://aws.amazon.com/blogs/machine-learning/use-aws-deep-learning-containers-with-amazon-sagemaker-ai-managed-mlflow/) for details 🔄
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- Deploy and serve Large Language Models efficiently on Amazon EKS using vLLM Deep Learning Containers for optimized inference performance and scalability. Checkout [Deploy LLMs Like a Pro on EKS](https://aws.amazon.com/blogs/architecture/deploy-llms-on-amazon-eks-using-vllm-deep-learning-containers/) for details 🚀
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- Learn to fine-tune and deploy Meta's Llama 3.2 Vision model for AI-powered web automation by combining AWS DLCs, Amazon EKS, and Bedrock to enable visual understanding in your applications. Checkout [Web Automation with Meta Llama 3.2 Vision](https://aws.amazon.com/blogs/machine-learning/fine-tune-and-deploy-meta-llama-3-2-vision-for-generative-ai-powered-web-automation-using-aws-dlcs-amazon-eks-and-amazon-bedrock/) for details 🎯
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- Discover how to simplify and accelerate your deep learning workflow by integrating AWS Deep Learning Containers with Amazon Q Developer and Model Composer Platform (MCP) for streamlined environment setup and management. Checkout [Supercharge Your DL Environment](https://aws.amazon.com/blogs/machine-learning/streamline-deep-learning-environments-with-amazon-q-developer-and-mcp/) for details ⚡
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- Discover how to simplify and accelerate your deep learning workflow by integrating AWS Deep Learning Containers with Amazon Q Developer and Model Context Protocol (MCP) for streamlined environment setup and management. Checkout [Supercharge Your DL Environment](https://aws.amazon.com/blogs/machine-learning/streamline-deep-learning-environments-with-amazon-q-developer-and-mcp/) for details ⚡
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