[D][P] ML Devs: what are your the pain point working in MLOps? Are these AutoML tools (Google AutoML, Amazon Sage-maker, huggingFace, Clarifai, Roboflow, datarobot, h20, etc) actually helpful for your work?

Vote up if you see pain points in current MLOPs!

I see many many people are complains the complexity and the mess in the mlops([https://www.reddit.com/r/MachineLearning/comments/pgbhz3/d\_ml\_devs\_what\_are\_your\_biggest\_painpoints\_when/](https://www.reddit.com/r/MachineLearning/comments/pgbhz3/d_ml_devs_what_are_your_biggest_painpoints_when/)) , to summarize it up:

1. tool management, such as GCP, AWS, etc, and all kinds of data annotation platform and data management
2. figure out the business value quickly
3. dont know how much data we need, and how much cost that would be.
4. lack of data

However, there are so many autoML …

amazon automl datarobot google huggingface machinelearning ml mlops pain tools work