Introduction

Introduction

Complete

Bridging the Gap: From Development to Production

Have you ever improved a model in development only to watch it disappoint in production? That gap between offline success and real-world performance is exactly what this unit tackles.

You will explore cross-validation, error analysis, calibration, and statistical testing so you can diagnose weak spots, validate changes, and choose refinements that truly matter. The goal is not just better scores; it is better decisions about what to improve.

Why does this unit matter?

In practice, teams need to weigh performance gains against resources, latency, complexity, and business impact. Refinement is not just a technical exercise; it is a decision-making process.

These techniques help you understand whether a model is genuinely getting better and whether the improvement is worth the trade-offs.

Learning objectives

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

• Analyse techniques for model testing and tuning and their impact on performance.
• Design testing frameworks that assess model performance across key dimensions.
• Evaluate the trade-offs between model complexity, hardware requirements, and performance.
• Implement model refinement strategies that optimise performance while maintaining business alignment.

Before you continue

Make sure you have completed Module 8 Unit 1: Performance Metric Selection and Implementation.

Action item: Pause and think

If a model is underperforming, what would you inspect first: the data, the errors, the confidence scores, or the evaluation setup? What would guide that decision?

Questions & reflections

1. Have you ever worked on, or thought about how you would approach, a model that needed improvement? What challenges did or would you expect to face?

Type your reflection here...

2. Imagine your model is performing below expectations. What steps would you take to figure out why, and how would you decide what to fix first?

Type your reflection here...