Applied AI Engineering Programme Guide

Empowering you to scope, prototype and share “quick win” ML services aligned with commercial KPIs. You'll start looking at worked examples of mathematics in the workplace.

• Workshop: Collaborative engineering with AI: Build and deploy a rapid MVP using public models
• Task: Reuse a containerised Proof-of-Concept model to create a prototype solving a real-world business problem. Show ROI to stakeholders; pitch the defined saving or uplift

Hands-on with Python, version control, and ML libraries. You'll learn to build reusable, production-ready code with emphasis on clean, test-driven development, and reusable components under best practices.

• Workshop: Pair-programming & debugging clinic for fun and profit
• Task: Refactor legacy code into a testable ML module that simulates real user action

Design cloud-based pipelines that combine performance with governance and cybersecurity best practices, linking technical design to cost and scalability. Use our booster modules to help with networking and other relevant knowledge.

• Workshop: Create architecture diagrams using infrastructure-as-code (IaC)
• Task: Produce a model for a data flow and assess risk

You'll master CI/CD containerisation and live-model monitoring. We'll focus on lifecycle management, staging, experiment tracking and live monitoring to keep AI services healthy and within robust pipelines, supported with incident-response procedures.

• Workshop: Build an end-to-end orchestration pipeline
• Task: Ship a model with MLFlow, stages, and alert threshold

Here we'll ease you into applying core maths (vectors, matrices, and more) and connect theory to real ML tuning problems and performance metrics.

• Workshop: Metric and model tuning lab (metric-driven optimisation)
• Task: Compare optimiser performance on a real dataset and simulate work with a data scientist

Regression, classification and evaluation techniques take centre stage. Bias-variance trade-offs and model-selection strategies prepare you for production scenarios. We also cover computer vision and evaluation strategies.

• Workshop: Classification in action: production-grade classifiers
• Task: Train, evaluate and document a API-ready supervised model with contracted tests and version tags

From data cleaning to feature extraction and stability testing, we'll show how strong features make-or-break model performance. You'll learn robust feature pipelines, drift checks and automated unit tests.

• Workshop: Influencing model success with model drift and data drift safeguard builds
• Task: Build a feature pipeline with automated checks. Create a feature store script with built-in monitoring and push it to the registry

You'll learn clustering, semi-supervised, ensemble and reinforcement learning, with an eye on when each approach delivers business value.

• Workshop: Putting it all together: model selection play-off
• Task: Recommend, justify, and publish a production candidate from a comparative dashboard

Demystify CNNs, RNNs, and transformers by designing, training, and debugging neural networks – weighing GPU cost against accuracy.

• Workshop: Build, train and benchmark a neural net
• Task: Tune hyper-parameters to beat a published baseline on a structured dataset

You'll push performance further while keeping costs low with transfer learning, fine-tuning and model pruning.

• Workshop: Optimise a pre-trained model with Hugging Face
• Task: Adapt a public model to a novel dataset and log the improvement curve

NLP and domain-specific AI solutions. You'll align technical deliverables with strategic outcomes, commercial goals and stakeholder needs.

• Workshop: Sentiment analysis for commercial insight
• Task: Build a text analytics service and integrate it with a Business Intelligence platform to produce a business impact memo

Retrieval-augmented generation, prompt-engineering and scalable LLM hosting, covering opportunities and risks of GenAI, covering RAG patterns and secure GenAI APIs operating in agentic architectures.

• Workshop: Hands-on GenAI deployment – rolling out an agentic LLM service
• Task: Deploy a small API-enabled RAG-powered service and quantify key metrics for optimisation

Fairness, visualisation and explainability (XAI) techniques help you communicate model behaviour to technical and non-technical audiences.

• Workshop: Explainability dashboard sprint & user-centric critical analysis
• Task: Produce a SHAP-based report and accompanying stakeholder slide deck

You'll scope your EPA project, practise stakeholder engagement and map career pathways, integrating all previous skills. We'll focus on integrating vs. decommissioning legacy systems, horizon scanning with technology scorecards, managing innovation with roadmaps and honing your research skills.

• Workshop: Project strategy & stakeholder masterclass
• Task: Finalise capstone documentation and evaluation

You'll practise presenting your project and answering competency-based questions. Review and refine your evidence, receive structured feedback, plan your revision, and commit to peer support to ensure you're fully prepared for end point assessment.