SageMaker Training Plans now enables extending of existing capacity commitments without workload reconfiguration

[SageMaker Training Plans](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html) allows you to reserve GPU capacity within specified time frames in cluster sizes of up to 64 instances. Today, Amazon SageMaker AI announces that Training Plans can now be extended when your AI workloads take longer than anticipated, ensuring uninterrupted access to capacity. You can extend plans by 1-day increments up to 14 days, or 7-day increments up to 182 days (26 weeks). Extensions can be initiated via API or the SageMaker console. Once the extension is purchased the workload continues to run un-interrupted without you needing to reconfgure the workload. SageMaker AI helps you create the most cost-efficient training plans that fits within your timeline and AI budget. Once you create and purchase your training plans, SageMaker automatically provisions the infrastructure and runs the AI workloads on these compute resources without requiring any manual intervention. See the [SageMaker AI pricing page](https://aws.amazon.com/sagemaker/ai/pricing/) for a detailed breakdown of instance availability by AWS Region. To learn more about training plan extensions, see the [Amazon SageMaker Training Plans User Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html)