How to Build ML Systems That Don’t Collapse When Moving From Notebooks to Production
9 min read22 hours ago
–
The Truth Nobody Tells You
87% of machine learning projects never reach production.
But it’s not because the models are bad.
It’s because the structure collapses.
You start with a Jupyter notebook. Messy but works. Then you need to share it with teammates. Then it needs to run on a schedule. Then it needs to handle real data. Then it breaks in production.
Most data scientists never learn this lesson because universities typically teach model building, rather than system building.
But production is where the real problems are.
And the difference between a failed project and a successful one isn’t the algorithm. It’s the folder structure.
[Non-membe…
How to Build ML Systems That Don’t Collapse When Moving From Notebooks to Production
9 min read22 hours ago
–
The Truth Nobody Tells You
87% of machine learning projects never reach production.
But it’s not because the models are bad.
It’s because the structure collapses.
You start with a Jupyter notebook. Messy but works. Then you need to share it with teammates. Then it needs to run on a schedule. Then it needs to handle real data. Then it breaks in production.
Most data scientists never learn this lesson because universities typically teach model building, rather than system building.
But production is where the real problems are.
And the difference between a failed project and a successful one isn’t the algorithm. It’s the folder structure.
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1. Why Structure Is Architecture, Not Organization
Before diving into the “what”, let’s understand the “why”.