One thing I am looking at is nbdev from fastai folks
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?
Would like to get to the Maturity Level 2 here
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?
Currently we train from Sagemaker notebooks, push models to S3 and create containers for model serving
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?
GrumpyPenguin23 both in general and clearml 🙂