Foundations: harnessing AI and machine learning

_{Month 1}

Fundamentals of machine learning and AI

• Introduction to ML and AI
• The ML lifecycle and mathematical foundations
• ML models and methods
• Emerging trends in ML and AI

_{Month 2}

Solving business problems with machine learning

• Applying ML to business problems
• Scoping and translating business needs into ML solutions
• Documentation, project, and lifecycle management in ML

_{Month 3}

Data ethics and responsible AI

• Ethical usage of AI and ML
• AI and sustainable practices

_{Month 4}

Data preparation and feature engineering

• Understanding variables and features in ML
• Feature engineering and selection techniques
• Data preprocessing and quality control
• Programming tools for ML data preparation

_{Month 5}

Hackathon: planning and preparing an AI solution

• Working together on a challenge that allows apprentices to use the skills they’ve learned so far
• Complemented by a group presentation

Application: building AI models with security best practices

_{Month 6}

Model engineering and training

• Model training fundamentals
• Training process optimisation
• Evaluating sources of bias

_{Month 7}

Model evaluation

• Performance metric selection and implementation
• Model refinement

_{Month 8}

Data security, privacy, and governance

• Security and privacy in machine learning
• Regulatory compliance and risk management
• Building a security-conscious ML culture

_{Month 9}

Hackathon: engineering an AI solution

• Further consolidating knowledge of AI and ML techniques with a practical group project
• Complemented by a group presentation

Deployment: monitoring and maintaining models

_{Month 10}

Model deployment

• Model deployment and risk management
• ML/AI platform architecture and resource allocation
• Security and compliance in model deployment

_{Month 11}

Monitoring, maintenance, continuous learning

• Performance management
• Model adaptation
• Scalability and capacity management
• Model lifecycle management

_{Month 12}

Stakeholder communication

• Stakeholder management
• Technical documentation for ML projects
• Inclusive collaboration and reporting in ML teams

_{Month 13}

Hackathon: deploying an AI solution

• Working in teams to deploy, monitor, and maintain an AI solution
• Complemented by a group presentation