Introduction to AI Engineering Projects

Overview

Artificial intelligence is transforming how organisations solve problems, deliver services, and create value. We believe successful AI solutions require more than selecting a model or writing prompts. They require effective planning, governance, engineering, and delivery.

This three-day instructor-led course introduces learners to the practical design, planning, governance, development, and delivery of AI engineering projects. Through a blend of instructor-led learning, hands-on labs, collaborative workshops, and a capstone project, learners explore the complete AI project lifecycle. Topics include identifying business opportunities, preparing data, understanding machine learning and generative AI, designing agentic AI systems, applying responsible AI governance, preparing solutions for production, and communicating effectively with stakeholders.

By the end of the course, learners will understand how successful AI projects move from concept to production while developing practical skills that can be applied within real organisations.

Prerequisites

There are no formal prerequisites for this course.

Learners should have:

• General digital and technical literacy
• An interest in artificial intelligence and emerging technologies
• Familiarity with business processes and problem-solving
• A basic understanding of data concepts

No advanced programming experience is required.

Target audience

This course is designed for:

• Aspiring AI engineers
• Software engineers and developers
• Technical consultants and solution engineers
• Data and analytics professionals
• Product managers and product owners
• Technical leads supporting AI initiatives
• Digital transformation and innovation professionals

It is ideal for learners who want to understand both the technical and organisational considerations involved in delivering successful AI solutions.

Objectives

By the end of this course, learners will be able to:

• Explain the role of AI engineering and how it differs from related disciplines
• Describe the end-to-end lifecycle of an AI engineering project
• Assess business challenges and identify opportunities where AI can deliver value
• Apply foundational Python and data handling techniques to support AI initiatives
• Distinguish between machine learning, deep learning, generative AI, and large language model solutions
• Design high-level agentic AI solutions using planning, memory, retrieval, and tool integration concepts
• Apply responsible AI governance, ethics, and risk management principles throughout an AI project
• Plan the transition from prototype to production-ready AI solutions
• Communicate AI project roadmaps and recommendations confidently to technical and non-technical stakeholders

Outline

Introduction to AI engineering

Explore AI engineering as a discipline and discover how AI solutions progress from concept through to production.

Topics include:

• What AI engineering is
• Roles and responsibilities within AI teams
• The AI project lifecycle
• Common delivery risks and challenges
• Cross-functional collaboration

Hands-on activity

• AI lifecycle mapping challenge

AI strategy and business value

Discover how organisations identify, prioritise, and deliver AI initiatives that create measurable value.

Topics include:

• Strategic drivers for AI adoption
• Evaluating AI opportunities
• Business value and return on investment
• AI roadmapping
• Stakeholder engagement

Hands-on activity

• Pitch your AI strategy

Python and data skills

Build the practical data skills that support successful AI engineering projects.

Topics include:

• Python fundamentals
• Working with Pandas
• Data cleaning and transformation
• Data validation
• Exploratory data analysis
• Data visualisation fundamentals

Hands-on lab

• QuickBite delivery delays data exploration

Machine learning and deep learning essentials

Understand the core AI techniques that underpin modern intelligent systems.

Topics include:

• AI terminology and categories
• Supervised learning
• Unsupervised learning
• Deep learning fundamentals
• Generative AI
• Large language models
• Model evaluation
• Common implementation risks

Hands-on lab

• Machine learning method match-up and error spotting

Designing agentic AI solutions

Explore how modern AI agents are designed to solve increasingly complex tasks.

Topics include:

• Agentic AI fundamentals
• Planning and reasoning
• Tool integration
• Retrieval techniques
• Memory and context management
• Human-in-the-loop design
• Emerging architectural patterns

Hands-on activity

• Design your agentic AI system

Responsible AI governance

Learn how governance, ethics, and regulatory considerations support trusted AI adoption.

Topics include:

• Responsible AI principles
• Ethical risks and mitigation strategies
• Legal and regulatory considerations
• Transparency and explainability
• Privacy and security
• Governance frameworks

Hands-on activity

• Govern or redesign?

From prototype to production

Understand the practical considerations involved in deploying AI solutions into production environments.

Topics include:

• Prototype versus production systems
• Reliability and scalability
• Monitoring and observability
• Security and compliance
• Operational ownership
• Production readiness assessments

Hands-on lab

• Build your transition plan

Capstone AI engineering project

Bring together the concepts covered throughout the course by designing an end-to-end AI engineering solution.

Topics include:

• AI project planning
• Solution design
• Governance and risk management
• Stakeholder communication
• Roadmap creation
• Production planning

Hands-on activity

• AI project planning and stakeholder presentation

Exams and assessments

There are no formal examinations associated with this course.

Learners demonstrate their understanding through practical labs, collaborative workshops, knowledge checks, and a capstone project that brings together the concepts covered throughout the course.

Hands-on learning

Throughout the course learners will:

• Complete practical labs using realistic AI scenarios
• Participate in collaborative workshops and design activities
• Analyse real-world AI engineering challenges
• Develop an end-to-end AI project proposal
• Receive guidance and feedback from an experienced instructor