Data & ML · Do Directory
Top MLOps engineers, active now
MLOps is what keeps AI working after launch. It covers deploying models reliably, monitoring accuracy and cost, retraining when data drifts, rolling back bad releases and keeping a record of which model made which decision.
You need it once an AI feature matters to customers or revenue. Hire someone who keeps things as simple as your scale allows and sets up clear alerts and runbooks your team can follow.
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Questions
MLOps: common questions
When do we need MLOps rather than just a developer?
When a model is live and people depend on it, or when you have more than one model to look after. If nobody can say how accurate it is this month or what it costs, it’s time.
What is model drift?
It’s when the real world changes and a model’s predictions quietly get worse: new products, new customer behaviour or a supplier changing a data format. Monitoring catches it early so you can retrain before it costs you.
Can we avoid being locked into one provider?
Partly. Ask the engineer to keep your data, model files and configuration somewhere you control and to document how to move them. Some lock-in is a fair trade for simplicity, as long as it’s a choice rather than an accident.
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