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.
The article discusses insights gained from a deeper examination of Structured Outputs when dealing with messy, incomplete data. It highlights that even when a large language model returns perfectly formatted JSON, the content can still be incorrect. The author reflects on the implications of this observation for data science practices.
A reproducible 100-step LoRA fine-tuning run for OpenVLA, with dataset checks, Colab setup, training metrics, and W&B evidence. The post I Tried Fine-Tuning a Robot AI Model on Colab.
The analytics career I signed up for five years ago doesn't exist anymore, and honestly, I am fine with that. The post How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI appeared first on Towards Data Science .
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 .
The article discusses the effectiveness of stacking in machine learning, noting that it only provides benefits when the individual models involved make different mistakes. It highlights that stacking can improve performance under these conditions, but may not be advantageous if the models are similar in their errors.
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
What an ID column, a date string, and a missing value taught me. The post My Machine Learning Pipeline Cheated, and Cross-Validation Never Noticed appeared first on Towards Data Science .
The article discusses how even a well-constructed Marketing Mix Model (MMM) can produce inaccurate results because the data it relies on lacks essential information. It explores the idea that adjusting the timing of marketing spend, rather than the amount, might correct these inaccuracies. The piece highlights the limitations of MMMs when the underlying spend data is incomplete or misaligned with actual campaign dynamics.
The article describes how the author built an incremental data pipeline using dbt snapshots to monitor the job market they are attempting to enter. It also discusses the challenges faced with an API that provided misleading information during the process. The post was published on Towards Data Science.
Using mathematical optimization to solve a pickup-and-delivery problem with time windows. The post “Los Movimientos”: The Routing Problem That Nearly Broke My Spirit appeared first on Towards Data Science .
The article titled "Put Your Own Logic Inside the Codex Agentic Loop" discusses how to incorporate custom logic into Codex hooks, as described in the post on Towards Data Science.
The article explains how LoRA fine‑tuning addressed an under‑labeling issue in the SigLip model. It outlines that whether this approach is suitable depends on three specific questions. The post provides guidance on evaluating the appropriateness of fine‑tuning for your own use case.