From the options, From Roadmap to Reality: A Look at My MLOps Project’s Key Learnings

Building an MLOps Pipeline: A Step-by-Step Adventure 🚀

Been on a cool adventure building my own end-to-end MLOps pipeline for making predictions! It’s like creating a step-by-step recipe for training and using machine learning models. 🧑‍🍳

One big thing I learned is that you can’t build a house without a strong base. 🏠 So, I decided to really focus on getting everything working smoothly on my own computer using Docker Compose first. This helps me make sure all the key parts – like the system that tells the pipeline what to do (Airflow ⚙️), the tool that keeps track of my experiments (MLflow 📊), and the way to actually use the trained model (FastAPI 🚀) – work well together. Think of it as making sure all the ingredients taste good before you serve the dish to everyone! 🍽️

While the project is still getting there (still working on some parts like automatic retraining and keeping an eye on data changes), it’s becoming a solid way to see how all these pieces fit together in the real world. 🧩

I’d love to hear your thoughts and get your ideas as I keep building this. Feel free to check it out and even contribute if you’re interested! 🙏

Check out the project here: https://github.com/shisian512/mlops

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