Summary
In this chapter, we set up and configured our containerized ML environment using Kubeflow, Kubernetes, and Amazon EKS. After setting up the environment, we then prepared and ran a custom ML pipeline using the Kubeflow Pipelines SDK. After completing all the hands-on work needed, we proceeded with cleaning up the resources we created. Before ending the chapter, we discussed relevant best practices and strategies to secure, scale, and manage ML pipelines using the technology stack we used in the hands-on portion of this chapter.
In the next chapter, we will build and set up an ML pipeline using SageMaker Pipelines—Amazon SageMaker’s purpose-built solution for automating ML workflows using relevant MLOps practices.
