Are you just interested in the training part and managing the trained models, or you'd actually like to productionize the models and serve them at scale?

A lot of end-to-end platforms are available nowadays that try to cover the entire lifecycle of a model from data prep, ETL, to training, serving, monitoring, operating. However, I found none of them really robust enough to cover all these cases perfectly, so I resorted to using different pieces from different vendors combined with my own stuff to make the entire platform suit my needs. This is still not perfect, though, and I think there's a lot of room for improvement in the space to enable really easy to use and scalable MLOps.

Still some of the tools I found to be ok: TensorFlow TFX, Kubeflow (to some extent - ops are a nightmare), Feast, MLFlow, GCP Vertex and AWS Sagemaker can get some work done, too.