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

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

03:30 104,831 views

End-to-End Machine Learning Project – AI, MLOps

End-to-End Machine Learning Project – AI, MLOps

03:30 137,675 views

PyTorch for Deep Learning & Machine Learning – Full Course

PyTorch for Deep Learning & Machine Learning – Full Course

03:30 756,913 views

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

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

03:30 229,871 views

Production-Grade ML Project Tutorial – Build & Deploy

Production-Grade ML Project Tutorial – Build & Deploy

03:30 262,726 views

End To End Machine Learning Project Implementation With Dockers,Github Actions And Deployment

End To End Machine Learning Project Implementation With Dockers,Github Actions And Deployment

03:30 568,021 views

Top 5 Full Stack Machine Learning Project Ideas | Best ML Projects Ideas For 100% Placement

Top 5 Full Stack Machine Learning Project Ideas | Best ML Projects Ideas For 100% Placement

01:38 920,031 views

Build a Complete End-to-End GenAI Project in 3 Hours

Build a Complete End-to-End GenAI Project in 3 Hours

03:30 919,374 views

9 Machine Learning Projects Implementation | Complete ML Projects Using Python & Scikit-Learn

9 Machine Learning Projects Implementation | Complete ML Projects Using Python & Scikit-Learn

03:30 702,771 views

4 *Real* Machine Learning Projects That Get You Hired - No More Tutorials!

4 *Real* Machine Learning Projects That Get You Hired - No More Tutorials!

13:05 940,488 views

🎓 Featured Course Guide & Lesson Notes: Build & Deploy ML Churn model with FastAPI, MLFlow, Docker, & AWS

Top Result #1

Instructor: Anas Riad  |  Runtime: 03:30 mins  |  Students / Views: 104,831

🎁 FREE: 10 filters I run every job through (download): https://www.anasriad.com/upwork-checklist 📘 Full course (30% off with code UPWORK30): https://www.anasriad.com/become-a-top-1-freelancer-on-upwork 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 Brand Inquiries: https://www.passionfroot.me/anas-riad Key Highlights & Overview: Mastering <strong>Full Stack Machine Learning Project</strong> requires clear step-by-step guidance and practical hands-on visual demonstrations. Follow along with top-rated video tutorials from industry experts today on <strong>TutorTube</strong>.