Help a CS student. Need honest feedback on wrangling data for ML/MLOps

I’m currently speaking with post-training/ML teams at LLM labs, folks who wrangle data for models or work in ML/MLOps.

Tell me your thoughts or anecdotes on ::

• Biggest recurring bottleneck (collection, cleaning, labeling, drift, compliance, etc.)
• Has RLHF/synthetic data actually cut your need for fresh domain data?
• Hard-to-source domains (finance, healthcare, logs, multi-modal, whatever) and why.
• Tasks you’d automate first if you could.