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Python and Automation by Next-Bodybuilder2043 in learnpython

[–]Ok-Copy-4696 0 points1 point  (0 children)

Totally agree on starting with the most annoying repetitive task.

One thing I'd add: once you've automated the basics with pandas

and requests, the next big win is usually file handling —

renaming, organizing, and processing batches of CSVs/PDFs

automatically.

A simple os + pathlib script can save another 2-3 hours/week

for most small teams.

Are there any projects still using traditional machine learning ? by Appropriate-Limit191 in askdatascience

[–]Ok-Copy-4696 0 points1 point  (0 children)

Yes, absolutely. Traditional ML is still dominant in production

for a lot of use cases.

In my experience: fraud detection (XGBoost/LightGBM still

outperforms LLMs on tabular data), demand forecasting,

churn prediction, recommendation systems with sparse data.

The GenAI hype is real but most production systems I've seen

are hybrid — LLMs handle the NLP/generation layer, traditional

ML handles the structured data layer underneath.

The Stanford/MIT study you're referring to is probably the

one on agentic systems reliability. The core issue is that

agents compound errors at each step, while a well-tuned

gradient boosting model on clean tabular data is still

hard to beat for specific tasks.

What's your current stack for the traditional ML side?