Introduction

Instruction and application

In Progress

Introduction

From the moment a model is deployed, the countdown begins.

Data changes. Behaviour shifts. The environment evolves. How long before your model loses its edge, and how will you know when it happens? Many teams treat deployment as the finish line, but in production, it's just the beginning.

This unit shifts your focus to what happens after deployment: real-time monitoring, detecting drift, and maintaining long-term integrity through automated testing and retraining.

Why does this unit matter?

In the real world, ML models don’t stay accurate forever. What works today might fail tomorrow as the world changes. These shifts can quietly erode performance, leading to inaccurate predictions and lost value.

Monitoring and adaptation are critical to spot early warning signs of drift. You’ll learn to design dashboards, detect subtle data shifts, and create maintenance strategies that ensure your models remain robust and trustworthy.

Learning objectives

By the end of this unit, you will be able to:

• Implement systems to detect and measure model drift in production.
• Analyse data drift patterns to determine their impact on performance.
• Design monitoring dashboards that track key performance metrics.
• Interpret test results to assess model performance against requirements.
• Develop maintenance protocols to ensure continued stability.
• Implement automated testing for continual learning models.

Action item: Pause and think

Before diving into the unit, take a moment to reflect on your current practice using the form below.

Questions & reflections

How would you currently detect if your model's performance is degrading in production?

Type your reflection here...

What would it take to build a system that catches model drift before it impacts business outcomes?

Type your reflection here...