Automate ML Retraining with Drift Detection | MLOps Project #machinelearning #ai

About this lesson

Support us: https://buymeacoffee.com/iquantconsult GitHub Repo: https://github.com/iQuantC/Data_Model_Drift_Detection_Retraining_Workflow 📝 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! 🎉

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