09 Model Evaluation & DagshubModel Evaluation with MLflow & Dagshub: The Real-World Teenage ML Engineer's Playbook Dagshub: The “GitHub for Data Science” That Actually Gets It Alright, first up-let’s talk about Dagshub. If you’re tired of losing track of your models, datasets, o...May 4, 2025·4 min read
04 What You Need: Prerequisite Files OverviewBuilding robust machine learning pipelines is more than just model training—it's about maintainability, traceability, and reproducibility. In this article, I'll walk you through how to set up structured logging and utility functions for configuration...Apr 18, 2025·3 min read
03 Project StructureA well-structured project makes it easier to scale, debug, and maintain code. Below is the directory structure of Predict-Pipe: Predict_Pipe/ │── .github/workflows/.gitkeep │── config/ │ ├── config.yaml │── params.yaml │── main.py │── Dockerfile │─...Apr 2, 2025·2 min read
02 Setting EnvironmentBefore discussing into the implementation, we need to set up our working environment properly. This includes creating a virtual environment, installing dependencies, and configuring essential filees 1. Creating a Virtual Environment Using a virtual e...Apr 2, 2025·2 min read
01 IntroductionImagine building a powerful machine learning model that can predict wine quality with impressive accuracy. But what happens next? How do you ensure this model remains reliable, scalable, and easy to maintain in production? That’s where Predict-Pipe c...Apr 2, 2025·2 min read