From 03426f55be853331c79a189507d9d2cf2627a123 Mon Sep 17 00:00:00 2001 From: Adeel Hassan Date: Wed, 7 Aug 2024 12:01:31 -0400 Subject: [PATCH] update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1903f3399..bbf569c8d 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ It has built-in support for chip classification, object detection, and semantic **As a low-code framework**, Raster Vision allows users (who don't need to be experts in deep learning!) to quickly and repeatably configure experiments that execute a machine learning pipeline including: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment. ![Overview of Raster Vision workflow](docs/img/rv-pipeline-overview.png) -Raster Vision also has built-in support for running experiments in the cloud using [AWS Batch](https://github.com/azavea/raster-vision-aws). +Raster Vision also has built-in support for running experiments in the cloud using [AWS Batch](https://docs.rastervision.io/en/stable/setup/aws.html#running-on-aws-batch) as well as [AWS Sagemaker](https://docs.rastervision.io/en/stable/setup/aws.html#running-on-aws-sagemaker). See the [documentation](https://docs.rastervision.io/en/stable/) for more details.