Analyzing open-source ML pipeline models in real time using Amazon SageMaker Debugger

Open-source workflow managers are popular because they make it easy to orchestrate machine learning (ML) jobs for productions. Taking models into productions following a GitOps pattern is best managed by a container-friendly workflow manager, also known as MLOps. Kubeflow Pipelines (KFP) is one of the Kubernetes-based workflow managers used today. However, it doesn't provide all the functionality you need for a best-in-class data science and ML engineer experience. A common issue when developing ML models is having access to the tensor-level metadata of how the job is performing.