Hello everyone! 👋 I've been trying to bridge the gap between data scientists' lab work in Python notebooks and the massive challenge of operationalizing those models at scale. 🚀 I realized I understood creating an AI model, but had a huge blind spot on how you ship it, automate it, and monitor it for drift once it's live in production. So, to figure it out, I went down the MLOps rabbit hole, playing with Docker, MLflow, CI/CD (GitHub Actions is magic! ✨), and AWS. I wrote up this article detailing that journey: it's everything I wish I knew about the "factory floor" before starting an AI deployment project. If you're short on time, there's a 5-minute summary video inside. Any feedback is welcome!

very thorough and nicely written, great job!

Thank you so much @Miriam Aguirre