Drift Happens: How to Build a Drift Detection Pipeline on AWS and Keep Your ML Models Sharp!

So, you’ve built a cool machine learning model, maybe something like Random Forest or XGBoost, and it’s crushing predictions — until one day, BAM! It starts going off course. That’s drift, my friend — both feature drift and concept drift creeping in to mess with your model. But don’t worry! We’re going to build a slick drift detection and retraining pipeline on AWS to keep your model fresh and accurate.

Step 1: Understanding the Drift Types

• Feature Drift: This happens when the distribution of your input features (the data used to make predictions) changes over time. Your model is seeing different data than what it was trained on.
• Concept Drift: The relationship between input data and the target (what you’re trying to predict) shifts. For example, what used to be a strong indicator of success might no longer be relevant.

AWS has all the tools you need to detect these shifts and automatically retrain your models. Let’s dive into building that pipeline!

Step 2: Detecting Drift with AWS Modules

AWS SageMaker is your new best friend here! Here’s how to leverage its components for drift detection.

1. Data Collection and Storage: Amazon S3