AWS recently released the new Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like databases or message queues simply by using the Kubernetes API. The new SageMaker ACK Operators make it easier for machine learning (ML) developers and data scientists who use Kubernetes as their control plane to train, tune, and deploy ML models in Amazon SageMaker without signing in to the SageMaker console.
Kubernetes and SageMaker
Building scalable ML workflows involves many iterative steps, including sourcing and preparing data, building ML models, training and evaluating these models, deploying them to production, and monitoring workloads after deployment.
SageMaker is a fully managed service designed and optimized specifically for managing these ML workflows. It removes the undifferentiated heavy lifting of

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