"#abtest-mlops"

Task Overview

The tasks for this project are divided into several key components:

A/B Testing Framework:

• Understanding A/B testing principles
• Classical vs. sequential A/B testing analysis
• Statistical significance and hypothesis testing

A/B Testing with Machine Learning:

• Setting up MLOps environment
• Data versioning and reproducibility
• Training ML models (Logistic Regression, Decision Trees, XGBoost)
• Hyperparameter tuning and model evaluation

Interpretation & Reporting:

• Comparing A/B testing and machine learning approaches
• Presentation of analysis results
• Recommendations and limitations

Outputs