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In this post, we show how ML engineers familiar with Jupyter notebooks and SageMaker environments can efficiently work with DevOps engineers familiar with Kubernetes and related tools to design and maintain an ML pipeline with the right infrastructure for their organization. This enables DevOps engineers to manage all the steps of the ML lifecycle with the same set of tools and environment they are used to.
Originally appeared here:
Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes
Go Here to Read this Fast! Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes
