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IJONIS
Enterprise AIMLOps

MLOps

MLOps

MLOps (Machine Learning Operations) applies DevOps principles to the entire ML model lifecycle: training, validation, deployment, monitoring, and retraining. MLOps ensures that AI models don't just work in the lab but operate reliably, versioned, and automated in production.

Why does this matter?

Most AI projects fail not at the prototype stage but during the transition to production. MLOps closes this gap: automated deployment, continuous monitoring for model drift, and structured retraining ensure your AI models work reliably long-term — not just in the demo.

How IJONIS uses this

We set up MLOps pipelines with MLflow for experiment tracking, GitHub Actions for CI/CD, and Docker/Kubernetes for reproducible deployments. Model monitoring tracks performance metrics and triggers automatic retraining when accuracy falls below defined thresholds.

Frequently Asked Questions

Does a mid-sized company really need MLOps?
As soon as you have more than one AI model in production, yes. Without MLOps, you lose track of model versions, notice performance degradation too late, and spend more time on manual deployment than value creation. MLOps scales with you — from a simple pipeline to a fully automated platform.
What is model drift and why is it dangerous?
Model drift occurs when real data diverges from training data — for example, through changed customer behavior or new product categories. The model then delivers increasingly worse results without an obvious error. MLOps monitoring detects drift early and triggers retraining.
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