Main pain points in your ML day-to-day work (lack of good tools for your problem)

I’m just curious what are the things that are problems without a good solution that you face when working in the ML projects. For training models we have bunch of frameworks (e.g. transformers, PyTorch), for deployment many frameworks and cloud providers (e.g. TorchServe, NVIDIA Triton, BentoML), for orchestration is the same – many frameworks. Are there any blind spots that require building tools from scratch for your project? Maybe some tools are not generic enough and don’t cover custom needs of your project? Let me know 🙂

In the past projects I worked on I haven’t faced a situation where existing tools were not enough. Most problems were linked to the quantity or quality of data.