chore: Remove references to kubeflow

website/docs/blueprints/ai-ml/index.md

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 # AI/ML Platforms on EKS
 
-Running AI/ML platforms on Kubernetes can greatly simplify and automate the deployment, scaling, and management of these complex applications. There are a number of popular tools and technologies that have emerged to support this use case, including **TensorFlow**, **PyTorch** and **KubeFlow**.
+Running AI/ML platforms on Kubernetes can greatly simplify and automate the deployment, scaling, and management of these complex applications. There are a number of popular tools and technologies that have emerged to support this use case, including **TensorFlow**, **PyTorch**, **Ray**, **MLFlow**, etc.
+
 These tools make it easy to deploy AI/ML models in a containerized environment, and provide features such as automatic scaling, rolling updates, and self-healing capabilities to ensure high availability and reliability. By leveraging the power of Kubernetes, organizations can focus on building and training their AI/ML models, rather than worrying about the underlying infrastructure.
 With its robust ecosystem of tools and support for a wide range of use cases, Kubernetes is becoming an increasingly popular choice for running AI/ML platforms in production.
 
 The following Terraform templates are available to deploy.
 
 * [Ray on EKS](ray.md): This template deploys [RayCluster](https://docs.ray.io/en/latest/cluster/getting-started.html) on EKS.
-* [Kubeflow on AWS](kubeflow.md): This template deploys the [Kubeflow on AWS](https://awslabs.github.io/kubeflow-manifests/) distribution on EKS.
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 * [EMR NVIDIA Spark-RAPIDS](emr-spark-rapids.md): This template deploys the EMR NVIDIA Spark-RAPIDS blueprint with NVIDIA GPU Operator.