MAI study guide
MAI · Work through your exam, one topic at a time.
The MAI is a vendor-neutral certification-style credential for people who build, deploy, or govern artificial intelligence systems in an organisational setting. It suits machine-learning engineers moving from notebooks to production, data scientists who need to own the full lifecycle, technical product and project managers coordinating AI delivery, and risk, compliance, or governance staff who must review AI systems without writing the code themselves. The MAI study guide has 8 sections and about 2,602 words.
01Foundations of Artificial Intelligence and Machine Learning
This domain checks whether you can pick the correct learning paradigm for a problem, reason about generalisation instead of memorisation, and read a learning curve or score report without confusing the data splits or the metric. Supervised learning maps labelled inputs to outputs (classification, regression); unsupervised learning finds structure (clustering, dimensionality reduction, anomaly detection); reinforcement learning optimises a policy from reward signals over time.
339 words, about 2 min02Data Preparation, Feature Engineering, and Model Evaluation
How you turn raw, messy data into leakage-free features and how you choose an evaluation protocol that will still hold in production. Most failed models in practice fail here rather than in the algorithm.
316 words, about 2 min03Neural Networks and Deep Learning
Whether you understand what happens inside a network during forward and backward passes, and whether you can diagnose a training run that is diverging, stalling, or overfitting from the symptoms it shows. A layer computes z = Wx + b, then a = f(z); backpropagation applies the chain rule to get gradients for every parameter.
336 words, about 2 min04Generative AI and Large Language Models
How transformers produce text, how pretraining differs from fine-tuning and retrieval, and how to control cost, latency, and hallucination in a deployed LLM application. Self-attention computes weighted sums of value vectors, with weights from scaled dot products of queries and keys (divided by the square root of the key dimension).
325 words, about 2 min05AI Ethics, Safety, and Governance
Whether you can identify harm, measure fairness where metrics conflict, protect privacy, and place the controls - documentation, human oversight, red teaming - that regulators and reviewers expect before launch. Fairness metrics conflict.
321 words, about 2 min06AI Solution Design and Deployment
Whether you can move from a business decision to a working architecture - choosing serving patterns, meeting latency and cost targets, packaging the model, and releasing it safely. Start from the decision and the consumer , not the model: what action the output triggers, who reads it, and what each error type costs.
323 words, about 2 min07AI Operations, Monitoring, and Maintenance
What you watch after launch, how you tell benign input change from genuine degradation, and how you decide when to retrain rather than roll back. Monitor four layers: infrastructure (latency, error rate, GPU saturation), data (schema, ranges, null rates, distribution shift via PSI or KS), model (accuracy when labels arrive, calibration, PR-AUC), and business (conversion, cost, manual override rate).
309 words, about 2 min08Business Strategy and AI Project Management
Whether you can justify an AI investment in financial terms, sequence a portfolio of use cases, run delivery that survives contact with stakeholders, and connect model metrics to a business outcome. Build the business case on value (revenue, cost, risk, experience) against total cost of ownership: data acquisition, labelling, compute, integration, monitoring, and change management.
333 words, about 2 min