Why Most ML Models Die Before Production — How to Keep Them Alive?
Why Most ML Models Die Before Production — How to Keep Them Alive?
You trained a shiny model. 99% accuracy, ROC curve smooth as butter, validation loss hugging zero. But when it hits production, it crashes and burns. This is the sad fate of most machine learning models. But why? And more importantly how do you prevent it?
Let’s break it down simply.
1. Dependency Hell: It Worked on My Machine Syndrome
Models often rely on specific versions of libraries and frameworks. But when you push them to production, you hit various version conflicts, deprecated libraries, or platform-specific quirks that break everything. This classic dependency issue is one of the most common reasons for production failure.
What you should do:
- Containerize your model with Docker for environment consistency.
- Critically, align your development stack with the libraries and versions already running in production. Introducing new frameworks or versions that prod infra doesn’t support increases fragility and maintenance overhead.
Why Most ML Models Die Before Production — How to Keep Them Alive? was originally published in Artificial Intelligence in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.
