Automate ML Retraining with Drift Detection | MLOps Project

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GitHub Repo: https://github.com/iQuantC/Data_Model...


📝 Description:

In this hands-on MLOps project, I walk you through how to detect data drift in a machine learning model using Alibi Detect and automatically retrain the model using Apache Airflow.


🚀 What You'll Learn:

1. How to implement drift detection using statistical methods
2. Automate retraining pipelines with Apache Airflow
3. Version and save retrained models
4. Structure a real-world MLOps workflow from scratch


🛠 Tech Stack:

1. Python
2. Scikit-learn
3. Alibi Detect
4. Apache Airflow


📂 Project includes:
1. Training
2. Drift simulation
3. Airflow DAGs, and
4. Versioned model saving


💡 Ideal for:

1. Data Scientists wanting to break into MLOps
2. ML Engineers automating model workflows
3. Anyone building intelligent retraining pipelines

🧠 Have questions or want the full tutorial? Drop a comment below or subscribe for more real-world MLOps projects!



⏱️ Timestamps:

0:00 - Intro
00:54 - Components & Environment Setup
03:29 - Prepare Training Data
06:00 - Train and Save the ML Model
10:10 - Implement Drift Detection
16:06 - Setup Apache Airflow Environment with Docker Compose
22:33 - Create Airflow DAG to Automate Retrain Workflow
27:35 - Start Airflow Scheduler to Trigger Retrain Pipeline
31:25 - Summary & Clean up


#machinelearning #driftdetection #datadrift #datascience #alibidetect #airflow #mlops #ai


Disclaimer: This video is for educational purposes only. The tools and technologies demonstrated are subject to change, and viewers are encouraged to refer to the official documentation for the most up-to-date information.

Happy MLOpsing! 🎉