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“This is the time of year when data science teams across industries crunch numbers, submit annual reports, and plan goals and objectives for the coming year.
In other words, this is a great opportunity to delve into the often confusing world of metrics, KPIs, and evaluation methods, their pitfalls and their benefits. —There are many. The top articles we've selected for you this week tackle the challenge of generating reliable insights and avoiding common mistakes.
Why AI tuning starts with better evaluation
What do you do when your LLM tool doesn't give you the results you want? Why does a model perform well on public benchmarks but disappoint when applied to internal tasks? As Hailey Quach aptly puts it, “Tuning truly begins when you define what's important enough to measure and the methods you'll use to measure it.”
Metric deception: When the best KPIs hide the worst failures
A key lesson highlighted by Shafeeq Ur Rahaman in a recent article is that old data and bad code are (relatively) easy to fix. The real risk is having false confidence in a system that no longer measures what it was designed to track.
Everyday decision making is more complicated than you think — here's how AI can solve it
Separating signal from noise is perhaps the most important responsibility of every data scientist. As Sean Moran shows in his thorough primer on noise, this is easier said than done, but new tools can help you stay on the right track.
Most read articles this week
Check out three articles that have resonated with a wide range of readers over the past few days.
The next “big” language model may not be big after all, by Moulik Gupta
Data Science in 2026: Is it still worth it?, by Sabrin Bendimerad
I used Pandas to clean up messy CSV files. This is the exact process I follow every time. Written by Ibrahim Salami
Other recommended books
We hope you'll enjoy our recent must-reads on a variety of topics.
- Machine Learning and Deep Learning Advent Calendar Series: Blueprint, written by Angela See
- Water Cooler Chat, Episode 1 10: So What Happened to the AI Bubble?, by Maria Mushutzi
- 10 Lessons on Building LLM Applications for Engineers (written by Shuai Guo)
- Developing Human Sexuality in the Age of AI, by Stephanie Carmer.
- LLM-as-a-Judge: What it is, why it works, and how to use it to evaluate AI models (by Piero Paialunga)
In case you missed it: Latest Author Q&A
In the latest Author Spotlight, Vyacheslav Efimov talks about AI hackathons, data science roadmaps, and how AI is meaningfully changing the work of everyday ML engineers.
Introducing new authors
We hope you'll take the time to explore some of the great work from our newest group of TDS contributors.
- Nishant Arora wrote an interesting explanation of how AI will revolutionize car design.
- Aakash Goswami's debut article takes you behind the scenes of India's RISAT (Radar Imaging Satellite) program.
- Shashank Vatedka shared an incisive analysis of the risks (professional, social, and ethical) we take when we rely too heavily on AI-powered tools.
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