An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The MSc in MLOps and AI Engineering at Brookfield School of Computing is engineered to provide deep competency across core computer science, systems design, and targeted domain applications. Students develop fluency in software craftsmanship, distributed architectures, algorithm optimization, and data governance.
Instruction balances rigorous theoretical foundations with practical code repositories, laboratory simulations, and ethical technology review aligned with modern international computing standards.
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| MLO711 | Advanced Statistical Learning Theory & Optimization for MLOps and AI Engineering | Theory | Formulate empirical risk minimization, VC dimension, PAC learning bounds and non-convex gradient optimization. |
| MLO712 | High-Performance GPU Computing & PyTorch Distributed Lab | Practical | Implement multi-GPU distributed data-parallel and pipeline-parallel model training using PyTorch and CUDA. |
| MLO713 | Deep Generative Models, VAEs & Diffusion Architectures | Theory | Deconstruct score-based generative models, denoising diffusion probabilistic models (DDPM) and latent space math. |
| MLO714 | Tensor Computing, Kernel Optimization & Triton Workshop | Practical | Author custom GPU kernels, optimize memory bandwidth and fuse tensor operations using OpenAI Triton. |
| MLO715 | Mathematical Foundations of Probabilistic Graphical Models in MLOps and AI Engineering | Theory | Formulate Bayesian networks, Markov random fields, junction tree algorithms and variational inference in MLOps and AI Engineering. |
| MLO716 | Deep Representation Learning & Contrastive Self-Supervision Lab | Practical | Train self-supervised representations using SimCLR, CLIP, MoCo and masked autoencoders on large datasets. |
| MLO717 | Empirical AI Benchmarking, Reproducibility & Statistical Rigor | Theory | Design rigorous ablation studies, bootstrap confidence intervals and cross-dataset generalization benchmarks. |
| MLO718 | MLOps Pipeline Engineering: Tracking, Data Lineage & CI/CD | Practical | Construct automated ML pipelines using MLflow, DVC, Feast feature store and automated model regression gates. |
| MLO719 | AI Ethics, Algorithmic Auditing & European AI Act Compliance | Theory | Evaluate algorithmic bias metrics, disparate impact audits, model explainability and compliance with the EU AI Act. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| MLO721 | Transformer Architectures, Attention Mechanisms & Foundation Models | Theory | Analyze multi-head self-attention, rotary position embeddings (RoPE), flash attention and scaling laws in MLOps and AI Engineering. |
| MLO722 | Large Model Fine-Tuning & Quantization Lab (LoRA, QLoRA) | Practical | Fine-tune parameter-efficient large foundation models using LoRA, bitsandbytes 4-bit quantization and PEFT. |
| MLO723 | Reinforcement Learning from Human Feedback (RLHF) & Policy Optimization | Theory | Derive Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO) and reward modeling mathematics. |
| MLO724 | RLHF Alignment & Autonomous Agent Engineering Studio | Practical | Train preference reward models, align language agents and build tool-calling autonomous reasoning pipelines. |
| MLO725 | Geometric Deep Learning & Graph Neural Networks in MLOps and AI Engineering | Theory | Model non-Euclidean data structures using graph convolutional networks (GCNs), Message Passing and SE(3) symmetries. |
| MLO726 | Real-Time Model Serving, TensorRT & vLLM Inference Lab | Practical | Deploy ultra-low latency model serving clusters with vLLM, continuous batching and TensorRT-LLM acceleration. |
| MLO727 | Explainable AI (XAI), Mechanistic Interpretability & Feature Attribution | Theory | Deconstruct neural circuits, probing classifiers, Integrated Gradients and SHAP values for model transparency. |
| MLO728 | Adversarial Robustness, Red Teaming & Jailbreak Defense Studio | Practical | Simulate gradient-based adversarial attacks, test prompt injection vectors and implement robust defense guards. |
| MLO729 | Domain Applied AI Enterprise Practicum: End-to-End System | Practical | Engineer and validate an end-to-end intelligent predictive or generative system solving a complex industry problem. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| MLO731 | Frontiers in Multimodal Reasoning & Embodied Intelligence | Theory | Critically assess vision-language-action (VLA) architectures, cross-modal attention and physical spatial reasoning. |
| MLO732 | Multimodal Vision-Language Model Engineering Lab | Practical | Fine-tune and deploy multimodal vision-language models for zero-shot spatial reasoning and visual question answering. |
| MLO733 | Strategic AI Leadership, Governance & Corporate AI Strategy | Theory | Formulate enterprise AI adoption roadmaps, calculate ROI on compute infrastructure and establish AI governance councils. |
| MLO734 | Continuous Learning, Model Drift Detection & Observability Lab | Practical | Implement real-time embedding drift detection, concept drift monitors and continuous fine-tuning pipelines. |
| MLO735 | Privacy-Preserving Machine Learning & Federated Learning | Theory | Derive differential privacy (DP-SGD) bounds, secure multi-party computation and federated averaging protocols. |
| MLO736 | MSc MLOps and AI Engineering Thesis Proposal Colloquium & Defense | Practical | Formulate and defend a formal scientific master's research proposal specifying experimental datasets and models. |
| MLO737 | Intellectual Property in Synthetic Media, Model Licensing & Tech Transfer | Theory | Analyze training data copyright precedents, open-weights model licensing and enterprise commercialization paths. |
| MLO738 | Pre-Thesis Neural Architecture Prototyping & Pilot Testing Lab | Practical | Construct initial experimental neural architectures and gather preliminary validation metrics for the thesis. |
| MLO739 | Industry Research Collaboration & Real-World Dataset Validation | Practical | Validate neural models against messy, real-world industry datasets under strict computational constraints. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| MLO741 | MSc MLOps and AI Engineering Dissertation: Empirical Methodology & Benchmark Datasets | Practical | Establish experimental baselines, data curation pipelines and statistical verification suites in MLOps and AI Engineering. |
| MLO742 | MSc MLOps and AI Engineering Dissertation: Neural Model Architecture & Implementation | Practical | Engineer the complete computational model embodying the novel algorithmic or architectural contribution in MLOps and AI Engineering. |
| MLO743 | MSc MLOps and AI Engineering Dissertation: Rigorous Empirical Evaluation & Ablation Analysis | Practical | Execute exhaustive empirical experiments, cross-dataset ablations and statistical significance validations. |
| MLO744 | Critical Literature Synthesis & State-of-the-Art Analysis in MLOps and AI Engineering | Theory | Synthesize top-tier conference publications (NeurIPS, ICML, CVPR, ACL) identifying clear theoretical gaps. |
| MLO745 | Scalability Benchmarking, Quantization & Model Audit Lab | Practical | Subject the dissertation model to latency profiling, memory consumption audits and security stress tests. |
| MLO746 | Scientific Manuscript Preparation & Peer Review in MLOps and AI Engineering | Theory | Author a publication-ready academic manuscript adhering to top-tier machine learning conference standards. |
| MLO747 | Executive AI Leadership & Enterprise Strategic Roadmapping | Theory | Translate cutting-edge AI breakthroughs into actionable C-suite technology strategies and innovation initiatives. |
| MLO748 | Final Technical AI Model Portfolio & Reproducible Model Weights | Practical | Package reproducible HuggingFace model cards, training checkpoints, evaluation scripts and documentation in MLOps and AI Engineering. |
| MLO749 | MSc MLOps and AI Engineering Thesis Oral Defense & Public Academic Viva Voce | Practical | Defend the novel scientific thesis and experimental model findings before an academic examination committee. |
Recognized Bachelor Degree in computing, engineering, mathematics, quantitative discipline or relevant technical equivalence. English proficiency IELTS 6.5+ or equivalent.
Brookfield School of Computing is an independent private education institution in formation. Academic programmes, entry requirements, fees and awards are subject to institutional review and applicable Swiss legal regulations.