Today, we announced the public preview of Amazon SageMaker Components for Kubeflow Pipelines. Machine learning (ML) developers using Kubeflow Pipelines can convert their existing pipeline steps to run on SageMaker with the SageMaker Components. For instance, ML teams can use SageMaker for managed training on Spot instances which will automatically set up model checkpoints to S3 so that you can pause and resume training from the last saved state. Other SageMaker features that are supported in Kubeflow Pipelines are built-in algorithms, managed distributed training, and hyperparameter tuning. In addition, SageMaker can change instance types with one parameter swap, replacing the complicated autoscaling config in Kubernetes.