Automate ML Retraining with Drift Detection | MLOps Project
Support us: https://buymeacoffee.com/iquantconsult
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! 🎉