Nbdev ia "neat" but it's ultimately another framework that you have to enforce.
Re: maturity models - you will find no love for then here 😉 mainly because they don't drive research to production
Your described setup can easily be outshined by a ClearML deployment, but sagemaker instances are cheaper. If you have a limited number of model architectures you can get tge added benefit of tracking your s3 models with ClearML with very little code changes. As for deployment - that's another story altogether.
Maybe some of the other silent lurkers here would like to comment?
One thing I am looking at is nbdev from fastai folks
Would like to get to the Maturity Level 2 here
Currently we train from Sagemaker notebooks, push models to S3 and create containers for model serving
Well in general there is no one answer. I can talk about it for days. In ClearML the question is really a non issue since of you build a pipeline from notebooks on your dev in r&d it is automatically converted to python scripts inside containers. Where shall we begin? Maybe you describe your typical workload and intended deployment with latency constraints?
GrumpyPenguin23 both in general and clearml 🙂
Hi TrickySheep9 , ClearML Evangelist here, this question is the one I live for 😉 are you specifically asking "how do people usually so it with ClearML" or really the "general" answer?