Learn Data Science With Mukeshram

When I started working with Drift, I quickly realized there was a gap between theory and what actually happens in practice. This post is about understanding model drift and setting up automated retraining. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Model Drift As I delved into the world of machine learning operations (MLOps), I encountered a critical challenge that can make or break the performance of a model in production: model drift. It's a phenomenon where the underlying relationships between the input data and the predicted outputs change over time, causing the model's accuracy to degrade. My experience with model drift has been a journey of discovery, filled with mistakes, lessons learned, and a deeper understanding of how to navigate this complex issue. Concept Drift and Data Drift There are two primary types of model drift: concept d...

When I started working with GitHub Actions, I quickly realized there was a gap between theory and what actually happens in practice. This post is about how i set up a ci/cd pipeline for ml models using github actions. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to CI/CD Pipelines for ML Models As I delved into the world of Machine Learning Operations (MLOps), I quickly realized the importance of implementing a robust Continuous Integration/Continuous Deployment (CI/CD) pipeline. This wasn't just about automating repetitive tasks; it was about ensuring the reliability and consistency of our ML models. In this article, I'll share my experience of setting up a CI/CD pipeline using GitHub Actions, highlighting the lessons I learned, the challenges I faced, and the solutions I discovered. Getting Started with GitHub Actions One of the primary reasons...