AWS sagemaker documentation change
Summary
Updated documentation link for JumpStart class reference
Security assessment
Change only modifies a documentation URL by removing a fragment identifier (#anchor). No security context, vulnerabilities, or security features are referenced or modified in this change.
Diff
diff --git a/sagemaker/latest/dg/how-it-works-training.md b/sagemaker/latest/dg/how-it-works-training.md index c1370c36e..bded8d7af 100644 --- a//sagemaker/latest/dg/how-it-works-training.md +++ b//sagemaker/latest/dg/how-it-works-training.md @@ -44 +44 @@ Description | Bring your data. SageMaker AI helps manage building ML models and -Optimized for | Low/no-code and UI-driven model development with quick experimentation with a training dataset. When you [build a custom model](./canvas-build-model.html) an algorithm automatically selected based on your data. For advanced customization options like algorithm selection, see [advanced model building configurations](./canvas-advanced-settings.html). | Training ML models with high-level customization for hyperparameters, infrastructure settings, and the ability to directly use ML frameworks and entrypoint scripts for more flexibility. Use built-in algorithms, pre-trained models, and JumpStart models through the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) to develop ML models. For more information, see [Low-code deployment with the JumpStart class](https://sagemaker.readthedocs.io/en/stable/overview.html#low-code-deployment-with-the-jumpstartmodel-class). | ML training workloads at scale, requiring multiple instances and maximum flexibility. See [distributed computing with SageMaker best practices](./distributed-training-options.html). SageMaker AI uses Docker images to host the training and serving of all models. You can use any SageMaker AI or external algorithms and [use Docker containers to build models](./docker-containers.html). +Optimized for | Low/no-code and UI-driven model development with quick experimentation with a training dataset. When you [build a custom model](./canvas-build-model.html) an algorithm automatically selected based on your data. For advanced customization options like algorithm selection, see [advanced model building configurations](./canvas-advanced-settings.html). | Training ML models with high-level customization for hyperparameters, infrastructure settings, and the ability to directly use ML frameworks and entrypoint scripts for more flexibility. Use built-in algorithms, pre-trained models, and JumpStart models through the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) to develop ML models. For more information, see [Low-code deployment with the JumpStart class](https://sagemaker.readthedocs.io/en/stable/). | ML training workloads at scale, requiring multiple instances and maximum flexibility. See [distributed computing with SageMaker best practices](./distributed-training-options.html). SageMaker AI uses Docker images to host the training and serving of all models. You can use any SageMaker AI or external algorithms and [use Docker containers to build models](./docker-containers.html).