Question 1

What is MLOps and why do we need it?

Accepted Answer

MLOps is the set of practices that keep machine learning models reliable after deployment. Without it, models degrade silently as data patterns shift. MLOps includes CI/CD pipelines for model updates, drift monitoring, automated retraining triggers, A/B testing for safe rollouts, and performance dashboards. It is the difference between a model that works on launch day and one that works six months later.

Question 2

How do you detect when a model's performance is degrading?

Accepted Answer

We instrument three monitoring layers. Data drift detection catches when input distributions shift from training data. Prediction drift tracks when model output patterns change unexpectedly. Business metric correlation links model performance to the KPIs you actually care about. Alerts trigger before users notice the problem.

Question 3

Can you add MLOps to models we already have in production?

Accepted Answer

Yes. Allerin retrofits MLOps infrastructure onto existing deployed models without retraining or redeploying them. We wrap your current models with monitoring, versioning, and rollback capability. Most retrofits take 4 to 6 weeks depending on how many models you're running.

Question 4

What MLOps platforms do you work with?

Accepted Answer

We build on MLflow, Kubeflow, SageMaker, Vertex AI, and custom Kubernetes-based pipelines depending on your existing cloud infrastructure. The tooling matters less than the practices. We pick the platform that fits your team's skills and your deployment environment.

MLOps & Model Operations

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