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With all the buzz around agents, LLMs, and the tools they power, it’s sometimes easy (or at least tempting) to think that fundamental machine learning workflows—feature selection, model monitoring, and the like—will soon become obsolete.
The articles we selected for you this week paint a different and more nuanced picture. Sure, practitioners have powerful new toys to play with, but the inherent value in understanding how models work (and break) isn’t going anywhere. Read on to learn how you can continue to develop as a data or ML professional in the “AI is everywhere!” age.
I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy
Walking us through the process she followed to excel at a recent competition, Claudia Ng highlights how “success in machine learning isn’t about having the fanciest tools or the most complex algorithms,” but rather all about “understanding your problem, applying solid fundamentals, and focusing on what actually moves the needle.”
Grad-CAM from Scratch with PyTorch Hooks
In a new hands-on tutorial, Conor O’Sullivan zooms in on a powerful explainable-AI technique for convolutional neural networks.
Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is
“I think the real concern is that most organizations monitor data without understanding it.” Mahe Jabeen Abdul explains why it’s so tricky to know what to monitor.
How to Measure Real Model Accuracy When Labels Are Noisy
Leveraging Bayesian methods and Python, Erdogan Taskesen’s deep dive focuses on effectively determining causality across variables.
This Week’s Must-Read Stories
Catch up on the articles our community has been buzzing about in recent days. Here’s a roundup of this week’s trending stories:
Model Context Protocol (MCP) Tutorial: Build Your First MCP Server in 6 Steps, by Destin Gong
Mobile App Development with Python, by Mauro Di Pietro
Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox, by Ugo Pradère
Other Recommended Reads
Why not branch out into some other fascinating areas in the wide world of data, ML, and AI?
Meet Our New Authors
Explore top-notch work from some of our recently added contributors:
- Sourav Mohile, a Mumbai-based data scientist, shared a comprehensive guide to regularization.
- Hugo Ducruc‘s debut article offers a thought-provoking reflection on the strides AI has made in recent years.
- Moulik Gupta unpacks the innovative memory function in the recently unveiled Titans architecture.
We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
