I’d love to hear examples of practical applications of NLP—whether it’s for sentiment analysis, chatbots, voice of customer, document parsing, or even internal knowledge search. What has worked, what hasn’t?

In the search for Machine Learning Engineer (MLE) roles, it’s becoming evident that a significant portion of these positions — though certainly not all — appear to favor candidates with PhDs over those with master’s degrees. LinkedIn Premium insights often show that 15–40% of applicants for such roles hold a PhD. Within large organizations, it’s also common to see many leads and managers with doctoral degrees.

This raises a concern: Is there an unspoken glass ceiling in the field of machine learning for professionals without a PhD? And this isn’t just about research or applied scientist roles — it seems to apply to ML engineer and standard data scientist positions as well.

Is this trend real, and if so, what are the reasons behind it?

Instruction-tuned models (e.g., GPT-4, Claude, Mixtral) perform well on many tasks out of the box. However, fine-tuning still has a place in specific domains. When and why would you still opt for fine-tuning over prompt engineering or RAG (retrieval-augmented generation)? Share your insights or examples.

In your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?
What indicators or monitoring strategies help you decide when updates are needed?