How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on The post My Fall-Detection Model Scored 94%, and It Was Lying to Me appeared first on Towards Data Science .
By Ramandeep Singh
The article recounts a final‑year project in which the author trained six different models for fraud detection. It highlights the discrepancy between the model that performed best on evaluation metrics and the one that was ultimately chosen for production. The piece reflects on how real‑world constraints can override purely statistical performance.
By Benjamin Nweke
The article recounts a production incident where a large language model (LLM) was used to evaluate the outputs of another LLM, and the judging model consistently agreed with itself. It explores the implications of relying on one model to assess another’s work, highlighting the potential pitfalls of such an approach. The narrative offers lessons on the limits of trusting automated evaluation systems in real‑world deployments.
By Priyansh Bhardwaj
Checking an A/B test until it crosses p < 0. 05 can turn a nominal 5 percent false-positive rate into almost 28 percent.
By Mila Sudarikova
But don't let the model check itself The post Design Loops, Not Prompts appeared first on Towards Data Science .
By Javier Marin
The article describes how to deploy a trained churn classifier as a FastAPI service so that other software can call it. It focuses on the practical steps needed to transform a model that performs well in isolation into a usable, callable API. The post is aimed at readers who want to make their machine‑learning models accessible in real-world applications.
By Ibrahim Salami