MLOps: reproducible ML pipelines, from feature store to production
IASWITCH industrializes your ML models with end-to-end pipelines: feature stores, reproducible training, model registry, automated deployment and drift monitoring (MLflow, Kubeflow, Ray).
In brief
- Reproducible training pipelines (Kubeflow, Ray)
- Feature stores and data versioning
- Model registry and automated deployment (MLflow)
- Drift monitoring (data drift, model drift)
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 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.
Got an AI project to ship to production?
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