To the people building MLOps systems: do you build them from the ground up or use managed MLOps platforms?

Managed MLOps platforms are services like Sagemaker, Databricks, and the managed versions of: MLFlow, MLRun, ZenML, where everything is set up for you. While building from the ground up would be provisioning your own infrastructure on a k8s cluster, and setting up all the components/integrations/workflows of your MLOps system with open-source frameworks/tools and CI/CD pipelines. As someone trying to break into an MLOps engineering role, which option do companies usually take and what is your experience with each of these? submitted by /u/throwaway18249 [link] [comments]