Introduction

Introduction

Complete

Introduction

This page introduces the core goals, expectations, and practical focus for this session. Read through it before moving into the activities below.

Imagine this: You’ve just wrapped up training an ML model that accurately predicts customer churn. The accuracy is strong, the code is clean, and the results look great in your Jupyter Notebook. Now leadership wants to deploy it — fast.

Suddenly, new questions come flying in:

• Can this model run in real time?
• What happens if the input data schema changes?
• How do we track whether the model is still working in three months?
• Can we roll back if something goes wrong? This is the moment when theory meets reality — and the success of your model depends not just on its training method, but also on how well it was deployed.

You’ve spent time learning how to build, train, and test models. Now it’s time to close the loop — by learning how to move from model development to model delivery. In practice, deploying machine learning (ML) models is not just a technical task — it’s a cross-functional effort that blends software engineering, data science, DevOps, and governance.

This unit introduces key practices such as containerisation, continuous integration/continuous deployment (CI/CD) for ML, monitoring, and model governance. You’ll see how modern teams use MLOps to streamline and scale their workflows, and why deployment is a discipline in its own right.

Mastering the fundamentals of deployment will equip you to design solutions that perform consistently, adapt to changing conditions, and earn the trust of users and stakeholders.

Why does this unit matter?

That moment of going from ‘model complete’ to ‘model deployed’ is one of the most critical — and often most overlooked — parts of the ML life cycle.

ML deployment is what translates your hard work into real-world impact. Without it, even the most accurate model stays locked in a notebook, unable to deliver value to the business.

In this unit, you’ll learn how to:

• Package models so users can deploy them consistently across environments.
• Automate deployment pipelines for reliability and speed.
• Monitor performance in production to detect issues before they become costly.
• Ensure traceability and reproducibility so your work can be trusted, audited, and improved. Whether you’re working in finance, health care, retail, or beyond, your ability to deploy models effectively is what turns ML experiments into production-grade solutions. And your skill in navigating that transition is what will set you apart in a competitive field.

Learning objectives

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

• Implement containerisation techniques for packaging ML models and their dependencies for deployment.
• Design deployment workflows that incorporate testing, versioning, and rollback mechanisms.
• Set up monitoring and logging systems for deployed ML models to track performance and detect issues.
• Apply model governance practices to ensure traceability and reproducibility in ML deployments.

Action item: Pause and reflect

Before diving in, reflect on your past experiences — or anticipated challenges — when moving from model development to deployment, by considering the questions below.

Questions & Reflections

What challenges have you encountered or do you anticipate when trying to move an ML model from your local machine to a system that can serve predictions to many users?

Are you familiar with concepts like containerisation (e.g., Docker) or CI/CD in software development? How might these apply to ML?

Why is it important to continuously monitor an ML model once it’s deployed, and what kinds of issues might arise if you don’t?