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‘Tis the season for data science teams across industries to crunch numbers, deliver annual reports, and plan goals and targets for next year.
In other words: it’s the perfect moment to dig into the often-messy world of metrics, KPIs, and evaluation methods, where the pitfalls — and the rewards! — are many. The top-notch articles we’ve selected for you this week tackle the challenges of producing reliable insights and avoiding common mistakes.
Why AI Alignment Starts With Better Evaluation
What do you do when your LLM tools fail to produce the desired results? Why would models perform well on public benchmarks but disappoint once you apply them to internal tasks? As Hailey Quach aptly puts it, “alignment genuinely starts when you define what matters enough to measure, along with the methods you will use to measure it.”
Metric Deception: When Your Best KPIs Hide Your Worst Failures
A key lesson Shafeeq Ur Rahaman drives home in his recent article is that stale data and bad code are (relatively) easy to fix; the real risk is having false confidence in a system that no longer measures what you’d designed it to track.
Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That
Separating signal from noise is perhaps the most essential responsibility of all data scientists. As Sean Moran shows in a thorough primer on noise, this is often easier said than done — but new tools can help you stay on the right path.
This Week’s Most-Read Stories
Catch up with three articles that resonated with a wide audience in the past few days.
Your Next ‘Large’ Language Model Might Not Be Large After All, by Moulik Gupta
Data Science in 2026: Is It Still Worth It?, by Sabrine Bendimerad
I Cleaned a Messy CSV File Using Pandas. Here’s the Exact Process I Follow Every Time., by Ibrahim Salami
Other Recommended Reads
We hope you explore some of our other recent must-reads on a diverse range of topics.
- The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint, by Angela Shi
- Water Cooler Small Talk, Ep. 10: So, What About the AI Bubble?, by Maria Mouschoutzi
- Ten Lessons of Building LLM Applications for Engineers, by Shuai Guo
- Developing Human Sexuality in the Age of AI, by Stephanie Kirmer
- 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: Our Latest Author Q&A
In our most recent Author Spotlight, Vyacheslav Efimov talks about AI hackathons, data science roadmaps, and how AI meaningfully changed day-to-day ML Engineer work.
Meet Our New Authors
We hope you take the time to explore some excellent work from the latest cohort of TDS contributors:
- Nishant Arora wrote a fascinating account of the ways AI could revolutionize car design.
- Aakash Goswami‘s debut article takes us behind the scenes of India’s RISAT (Radar Imaging Satellite) program.
- Shashank Vatedka shared a sharp analysis of the risks (professional, social, and ethical) we take on when we over-rely on AI-powered tools.
We Need Your Feedback, Authors!
Are you an existing TDS author? We invite you to fill out a 5-minute survey so we can improve the publishing process for all contributors.
