Build & Deploy ML Churn model with FastAPI, MLFlow, Docker, & AWS

Description

This is the first End-to-End project of this series. The aim is to build and deploy machine learning (and deep learning) models and focus on the whole pipeline (cleaning, training, serving layer, Docker container, CI/CD, pipeline, deploy and monitor, not just the traditional tutorials with everything done in a notebook. Tech stack and tools: - Great Expectation (data quality) - FastAPI (HTTP endpoint) - Docker (containerization) - MLFlow (ML experiment tracking) - GitHub Actions (CI/CD, run, test, deploy) - AWS ECS (Fargate) , and ALB Useful links: (Give the repo a star :) ) https://github.com/anesriad/Telco-Customer-Churn-ML https://www.kaggle.com/datasets/blastchar/telco-customer-churn TIMESTAMP: 0:00 - Project, Tools, & dataset overview 10:20 - Setup environment (clone or from scratch) 13:45 - EDA (clean, encode, ML models, hyperparameters) 33:22 - Modularise into Python scripts (MLFlow, pipelines, tests) 49:49 - FastAPI (HTTP endpoint) 56:06 - Docker (containers) 01:05:06 - CI/CD with GitHub Actions 01:08:55 - AWS deployment 01:20:00 - UI & testing live deployed ML model Next: ML end-to-end regression project. Add any suggestions in the comments or DM me on LinkedIn: https://www.linkedin.com/in/riadanas