MLOps: turn your AI prototype into a reliable tool that lasts
IASWITCH turns your machine learning models (forecasting, scoring, detection, classification) into reliable production tools: reproducible training, automated deployment and drift monitoring, so the model keeps performing over time.
In brief
- Your prototype becomes a service used every day
- Automated, traceable model updates
- Alerts as soon as quality drops (data drift)
- MLflow, Kubeflow, Ray
The model that worked… on the data scientist's laptop
Many AI projects stop at the prototype: a notebook with good results that nobody knows how to deploy, update or monitor. And an unmonitored model silently degrades as your data changes.
What is MLOps?
MLOps industrializes the machine learning model lifecycle: data preparation, reproducible training, versioning, deployment, monitoring and retraining. The goal is reliability and reproducibility in production.
Our end-to-end pipelines
We set up feature stores, reproducible training pipelines (Kubeflow, Ray) and a model registry (MLflow) that tracks every version. Deployment is automated and models are continuously monitored.
Drift monitoring
We instrument data drift and model performance detection, with alerts and, if needed, automated retraining to maintain quality over time.
Frequently asked questions
What is MLOps?
MLOps is the set of practices to industrialize ML models: reproducible training, versioning, model registry, automated deployment and drift monitoring in production.
How do you take an AI prototype to production?
By making training reproducible, versioning data and models, automating deployment and continuously monitoring quality. That is what keeps a model reliable once it is used every day.
How do you detect model drift in production?
By monitoring the input data distribution (data drift) and model performance (model drift) via dedicated metrics and alerts, with automated retraining when needed.
Which MLOps tools do you recommend?
MLflow for experiment tracking and the model registry, Kubeflow for pipeline orchestration on Kubernetes, and Ray for distributed compute.
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