Just kicked off my MLOps project! 🛠️ https://github.com/var1914/mlops-e2e-scratch
Curious how real-world data pipelines look at the enterprise level? 🤔
After researching how MLOps actually works in enterprise environments (not those oversimplified tutorials), I decided to build something authentic that showcases the messy realities of production ML.
Let's suppose you're thinking about starting your MLOps journey but aren't sure what real-world data downloading looks like... is it just simply getting raw data, or are there other factors at play?
Spoiler: It's WAY more complex than most tutorials suggest!
I've started with the first building block - a robust data management pipeline using Airflow that:
✅ Handles HuggingFace dataset downloads with proper retry logic (because APIs WILL fail)
✅ Implements rate limiting so we don't get banned (yes, this happens in production!)
✅ Uses queuing systems to manage parallel requests (critical when scaling)
✅ Runs everything in Docker containers with Kubernetes deployment configs
Still lots to add - I'm working on sharing downloaded data through persistent volumes and connecting this to downstream training pipelines. But this already demonstrates how messy and intricate real enterprise MLOps is!
This isn't some polished end product - it's a genuine look at the challenges we face in production ML. As I build this out, I'll keep sharing what I learn.
Anyone else building MLOps pipelines from scratch? Would love to hear your experiences!