Build Churn Training and Inference AI from Scratch - End to End Machine Learning Project - Part 1

Learn to build a comprehensive churn prediction system from the ground up in this hands-on tutorial that covers the complete machine learning lifecycle from data preprocessing to model deployment. Master the development of scalable ML architectures using industry-standard tools including Apache Airflow for workflow orchestration, MLFlow for experiment tracking, and Streamlit for creating interactive inference applications. Explore end-to-end system design principles, implement feature engineering techniques, and establish robust model training pipelines with automated retraining capabilities. Discover best practices for model performance optimization, system scaling, and production deployment while working through practical examples of ML services dependencies and training DAG configurations. Build expertise in creating optimized ML pipelines with advanced hyperparameter tuning and learn to validate results through interactive Streamlit applications that demonstrate real-world inference scenarios.