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Introduction

Instruction and application
Complete

Have you ever been part of an ML project that had the right people, data and ideas - but still struggled to stay on track? Delivering ML solutions is not only about code and models. It is about aligning people, processes and priorities toward a shared goal.

Intro illustration

ML project success depends on more than a good model. Leads and contributors need to plan, manage and communicate across technical and business boundaries, using methods that fit ML's iterative development, ongoing tuning and stakeholder uncertainty.

This unit gives you tools to manage those challenges with confidence: ML-aligned frameworks (including CRISP-style thinking and MLOps), risk and resource planning, and structured plans that create clarity and momentum - whether you coordinate a pilot or support a larger rollout.

Why does this unit matter?​

ML projects need coordination across teams, timelines and technology. Unlike many software projects, ML often involves changing data, evolving objectives and higher uncertainty. Strong project management is essential to turn good ideas into delivered outcomes.

Learning objectives​

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

  • Analyse project management methodologies and their fit for different ML initiatives.
  • Evaluate ML-specific risks and mitigation strategies within project frameworks.
  • Implement resource allocation and stakeholder management suited to ML delivery.
  • Design a project management plan that improves efficiency and effectiveness of an ML project.

Before you continue​

Complete:

  • Module 2 Unit 1: Strategic alignment and organisational readiness for ML.
  • Module 2 Unit 2: Scoping and translating business needs into ML solutions.

Pause and reflect​

Before diving in, think about how ML projects unfold in your organisation - or how they should. You will return to these ideas in the final activity.

Reflection
When an ML project moves from planning to execution in your workplace, what typically goes well - and what tends to go off track?
Not too clear about it.
How confident do you feel managing practical aspects of ML delivery (timelines, risks, stakeholder engagement)?
Not very confident.