Streamlining MLOps with GitHub Actions: My Journey to a Seamless CI/CD Pipeline
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...