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Phase 17 · MLOps & Model Deployment

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A/B Testing Models

Part of the AI Engineer Roadmap.

Summary

Running two model versions simultaneously on live traffic to compare real-world performance before fully committing to a rollout.

How to Learn This

1Design an A/B test comparing two model versions on a clear success metric.
2Learn how to split traffic safely and avoid contaminating your comparison.
3Understand statistical significance basics before declaring a 'winner.'

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Model Monitoring & Drift Detection

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Batch vs Real-time Inference

More topics in MLOps & Model Deployment

Model Serving (FastAPI/Flask)Docker for ML Models Model Versioning Experiment Tracking (MLflow, Weights & Biases)CI/CD for ML Pipelines Model Monitoring & Drift Detection Batch vs Real-time Inference Kubernetes for ML (Overview)Feature Stores (Overview)

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