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

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-Cus...
https://www.kaggle.com/datasets/blast...

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:
  / riadanas  

Brand Inquiries: https://www.passionfroot.me/anas-riad