[manca]

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.

[speedylight]

I'd say for the foreseeable future I simply want to focus training and running trained models, I don't plan to do anything at scale like launch a business, so the creating and training aspect is the one I want and probably should only focus on at first either way.

But I like your approach of stitching together various vendors so they fit your use case, I think it can be really flexible but also probably more expensive and slightly harder to manage... I think it can be worth the tradeoff though.

Thank you for the input!

[nathaliamdc]

You make a good point there. Personally I’ve struggled quite a bit moving from one off models to taking them to production. Would you mind elaborating on what you mean by none of the platforms being robust enough?