# 08 - A/B Testing ML Models: Methodology, Metrics, Implementation

Complete guide to A/B Testing ML Models: Methodology, Metrics, Implementation: architecture, practical implementation and best practices for developers and technical teams.

## What you'll learn

- ML A/B Testing vs Web A/B Testing: Critical Differences
- Experiment Design: Before the Code
- Defining Success Metrics
- Sample Size Calculation
- Traffic Splitting with FastAPI

*This article is part of the **MLOps** series on federicocalo.dev.*

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## Read the full article

The complete article (15 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [08 - A/B Testing ML Models: Methodology, Metrics, Implementation](https://federicocalo.dev/en/blog/ab-testing-ml-models-methodology-metrics-implementation)**

`https://federicocalo.dev/en/blog/ab-testing-ml-models-methodology-metrics-implementation`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*