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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 12 months.
In other words: it’s the proper moment to dig into the often-messy world of metrics, KPIs, and evaluation methods, where the pitfalls — and the rewards! — are many. The highest-notch articles we’ve chosen for you this week tackle the challenges of manufacturing reliable insights and avoiding common mistakes.
Why AI Alignment Starts With Higher Evaluation
What do you do when your LLM tools fail to provide the specified results? Why would models perform well on public benchmarks but disappoint when you apply them to internal tasks? As Hailey Quach aptly puts it, “alignment genuinely starts once you define what matters enough to measure, together with the methods you’ll 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 repair; the actual risk is having false confidence in a system that not measures what you’d designed it to trace.
On a regular basis Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That
Separating signal from noise is maybe probably the most essential responsibility of all data scientists. As Sean Moran shows in an intensive primer on noise, this is usually easier said than done — but latest tools can aid you stay on the appropriate path.
This Week’s Most-Read Stories
Meet up with three articles that resonated with a large audience up to now few days.
Your Next ‘Large’ Language Model Might Not Be Large After All, by Moulik Gupta
Data Science in 2026: Is It Still Value 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 Really useful Reads
We hope you explore a few of our other recent must-reads on a various 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 Concerning the AI Bubble?, by Maria Mouschoutzi
- Ten Lessons of Constructing LLM Applications for Engineers, by Shuai Guo
- Developing Human Sexuality within the Age of AI, by Stephanie Kirmer
- LLM-as-a-Judge: What It Is, Why It Works, and How you can Use It to Evaluate AI Models, by Piero Paialunga
In Case You Missed It: Our Latest Creator Q&A
In our most up-to-date Creator Highlight, Vyacheslav Efimov talks about AI hackathons, data science roadmaps, and the way AI meaningfully modified day-to-day ML Engineer work.
Meet Our Latest Authors
We hope you are taking the time to explore some excellent work from the most recent cohort of TDS contributors:
- Nishant Arora wrote an interesting account of the ways AI could revolutionize automobile design.
- Aakash Goswami‘s debut article takes us behind the scenes of India’s RISAT (Radar Imaging Satellite) program.
- Shashank Vatedka shared a pointy evaluation of the risks (skilled, social, and ethical) we tackle after we over-rely on AI-powered tools.
We Need Your Feedback, Authors!
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