For many teams, the real challenge isn’t building AI models, it’s getting them to actually work i...For many teams, the real challenge isn’t building AI models, it’s getting them to actually work i...
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Tolulade Ademisoye
• Oct 28
For many teams, the real challenge isn’t building AI models, it’s getting them to actually work in the real world.
I’ve seen companies spend months training a model, only to hit a wall at deployment. No monitoring. No feedback loops. No automation. Just a great experiment stuck in a notebook.
This is where MLOps matters.
Deploying, monitoring, and updating AI models at scale requires systems most organisations are still trying to figure out- automated pipelines, version control for models, data drift detection, and continuous retraining.
But when you get MLOps right?
Models improve faster. Teams move confidently. AI becomes more than a prototype; it becomes value.
I’m excited to be part of the group pushing this shift: turning machine learning into real-world impact
Because great models don’t change the world, deployed ones do.
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