diff --git a/docs/sagemaker/index.md b/docs/sagemaker/index.md
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--- a/docs/sagemaker/index.md
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@@ -8,7 +8,7 @@ Deep Learning Containers (DLCs) are Docker images pre-installed with deep learni
 
 Our DLCs are available everywhere [Amazon SageMaker](https://aws.amazon.com/sagemaker/) is [available](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/). While it is possible to use the DLCs without the SageMaker Python SDK, there are many advantages to using SageMaker to train your model:
 
-- Cost-effective: Training instances are only live for the duration of your job. Once your job is complete, the training cluster stops, and you won't be billed anymore. SageMaker also supports [Spot instances]((https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html)), which can reduce costs up to 90%.
+- Cost-effective: Training instances are only live for the duration of your job. Once your job is complete, the training cluster stops, and you won't be billed anymore. SageMaker also supports [Spot instances](https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html), which can reduce costs up to 90%.
 - Built-in automation: SageMaker automatically stores training metadata and logs in a serverless managed metastore and fully manages I/O operations with S3 for your datasets, checkpoints, and model artifacts.
 - Multiple security mechanisms: SageMaker offers [encryption at rest](https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest-nbi.html), [in transit](https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-in-transit.html), [Virtual Private Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/interface-vpc-endpoint.html) connectivity, and [Identity and Access Management](https://docs.aws.amazon.com/sagemaker/latest/dg/security_iam_service-with-iam.html) to secure your data and code.