ML Pipelines in Production: What Actually Matters
A practical look at building ML systems that work beyond notebooks. Data versioning, model registries, and monitoring — without the enterprise fluff.
⚡ What You'll Learn
- →Core concepts explained with real-world context
- →Practical implementation patterns
- →Common mistakes and how to avoid them
# ML Pipelines in Production
Building machine learning models in a notebook is one thing. Getting them to run reliably in production is another entirely.
The Gap
Most courses teach you how to train a model. They give you a clean CSV, you run fit(), and you get an accuracy score. Great. Now what?
In the real world, data comes in messy streams. Models drift. Latency matters. And "it works on my machine" is not a valid deployment strategy.
Key Components
- Data Validation: Is the data coming in today looking like the data you trained on yesterday?
- Feature Store: Are you calculating features consistently between training and inference?
- Model Registry: Which version of the model is currently running? Who approved it?
- Monitoring: Not just system metrics (CPU/RAM), but model metrics. Is the prediction distribution shifting?
Conclusion
Stop focusing solely on the algorithm. The best model in the world is useless if you can't ship it reliably.