ML Systems & Production Engineering
How models move from experimentation to scalable, maintainable
production environments. The backbone of applied AI engineering.
ML System Design &
Architecture
MLOps & Production Pipelines
Model Deployment &
Monitoring
API Design & System Reliability
Backend Engineering for AI
Model Evaluation &
Iteration
Perception & Representation
How machines understand images, language, and structured knowledge
to build meaningful representations of the world.
Computer Vision
Knowledge Representation &
Reasoning
Multimodal Systems
Structured Data Modelling
Unstructured Data Understanding
Feature Engineering
Foundation Models & Intelligent Systems
Exploring modern AI architectures and adapting them into usable
intelligent systems — from fine-tuning to agentic workflows.
Deep Learning Foundations
Large Language Models (LLMs)
Fine-tuning & Alignment
RAG & GraphRAG
Agentic Systems & MCP
Applied NLP Systems
Core ML & Mathematical Foundations
Building depth beyond tools and frameworks — understanding the
theory behind reliable, well-generalised AI systems.
Core ML Algorithms
Statistical Learning Principles
Optimisation Theory
Model Evaluation &
Generalisation
Linear Algebra & Calculus
Probability & Statistics
Responsible & Secure AI Systems
Engineering AI systems that are robust, ethical, and trustworthy —
ensuring they are safe, compliant, and socially responsible.
Differential Privacy
Federated Learning
Explainable AI
(XAI)
AI Security & Robustness
AI Ethics & Compliance
Bias Detection &
Fairness