AI Pro Mastery
From AI fundamentals to production-grade intelligent applications. Sixteen modules covering machine learning, deep learning, data science, generative AI, LLM engineering, RAG, vector databases, agents, agentic AI, MLOps and deployment — with a real project at the end of every one.
- Duration
- 10 – 12 months
- Level
- Beginner to advanced
- Modules
- 16
- Module projects
- 16
Roles it prepares you for
- AI Engineer
- ML Engineer
- Data Scientist
- MLOps Engineer
- Agentic AI Engineer
Real-world projects, not theory-only learning.
Each of the sixteen modules ends with something you built on real data and can demonstrate. The portfolio is the point of the programme, not a bonus at the end of it.
Built the way production AI actually gets built
Evaluation, guardrails, cost control and monitoring are taught alongside each capability — because those are what separate a prototype from a product.
Projects, not just lectures
Every module ends with something you built and can demonstrate. The portfolio is the outcome, not a bonus.
Production discipline throughout
Evaluation, guardrails, cost control and monitoring are taught alongside the capability, not bolted on at the end.
Live mentorship
Weekly live sessions, code review on your own work, and direct access to mentors who build these systems professionally.
Career preparation
Mock technical interviews, portfolio review and guidance on presenting AI work credibly to hiring panels.
Sixteen modules, sixteen projects
The complete syllabus, published in full. Each module lists what it covers and the project you will build with it.
AI Foundations
What AI can and cannot do today, how the field is structured, and how to tell a real use case from a demo.
- AI, ML and DL distinctions
- Model families
- Capability and limitation mapping
- Ethics and risk
- Use-case assessment
Project: Write a build-or-skip assessment for three real business scenarios.
Python for AI
Enough Python to build seriously — clean code, APIs, async work and environments you can reproduce.
- Language essentials
- Data structures
- Async and concurrency
- Working with APIs
- Packaging and environments
Project: Build a reusable Python client library for a public API, with tests.
Machine Learning
Supervised and unsupervised learning with an emphasis on honest evaluation rather than optimistic accuracy.
- Regression and classification
- Cross-validation
- Feature engineering
- Ensembles
- Clustering
Project: Train and evaluate a classifier on an imbalanced real-world dataset.
Deep Learning
Neural networks from first principles, then the architectures that matter in practice.
- Backpropagation
- Training dynamics
- CNNs
- Sequence models
- Transfer learning
Project: Fine-tune a pretrained model for a domain-specific task.
Data Science
The full analytical workflow on messy data: cleaning, exploring, visualising and communicating.
- Pandas and NumPy
- Statistics for decisions
- Visualisation
- Data quality
- Stakeholder communication
Project: Deliver an end-to-end analysis with a decision recommendation.
Generative AI
How generative models work, what they are genuinely good at, and where they quietly fail.
- Transformer architecture
- Sampling and temperature
- Multimodal models
- Hallucination mechanics
- Evaluation methods
Project: Build a content generation tool with a quality evaluation harness.
LLM Engineering
The engineering around the model call: streaming, caching, retries, routing and cost control.
- Provider APIs
- Streaming
- Semantic caching
- Retry and fallback
- Model routing
Project: Build a provider-agnostic LLM service layer with caching and failover.
Prompt Engineering
Prompts treated as code: structured, versioned, tested and measured against a dataset.
- Structured prompting
- Few-shot design
- Structured output and schemas
- Prompt versioning
- Regression testing
Project: Build a prompt test suite that scores changes against a gold dataset.
RAG
Retrieval-augmented generation done properly — grounding, citations and measurable retrieval quality.
- Chunking strategies
- Embedding models
- Hybrid retrieval
- Re-ranking
- Citation and grounding
Project: Build a RAG assistant over a large document set with cited answers.
Vector Databases
How vector search actually works, and how to run it at scale without blowing latency or budget.
- Embedding spaces
- Index types and trade-offs
- Metadata filtering
- Hybrid search
- Scaling and sharding
Project: Benchmark index configurations for recall, latency and cost.
AI Agents
Agents that use tools to finish tasks, with validation, limits and safe failure built in.
- Tool schema design
- Execution loops
- Error recovery
- Step and cost limits
- Permission scoping
Project: Build a tool-using agent that completes a real operational task.
Agentic AI
Multi-agent systems with planning, memory, delegation and review — and the observability to debug them.
- Planner, worker, reviewer roles
- Shared memory
- Message passing
- Re-planning on failure
- Tracing
Project: Build a multi-agent workflow with full step-level tracing.
MLOps
Versioning, pipelines, registries and monitoring — the discipline that keeps models working after launch.
- Data and model versioning
- Reproducible pipelines
- Model registry
- Staged promotion
- Drift monitoring
Project: Build a pipeline with experiment tracking and a model registry.
AI Deployment
Serving models and AI features in production with the latency, reliability and scaling users expect.
- Serving patterns
- Containerisation
- Autoscaling
- Latency optimisation
- Canary releases
Project: Deploy an AI service with autoscaling and a canary release.
AI Automation
Embedding AI decisions into business workflows with thresholds, fallbacks and audit trails.
- Workflow design
- Confidence thresholds
- Human-in-the-loop
- Exception queues
- Audit logging
Project: Automate a document-driven business process end to end.
Advanced AI Projects
A substantial portfolio system, built and defended the way you would in a senior technical interview.
- Architecture design
- Evaluation strategy
- Cost modelling
- Documentation
- Technical defence
Project: Ship and present a production-grade AI system of your own design.
What you will be able to do
Stated as capabilities you can demonstrate, not topics you will have been shown.
- Build and evaluate machine learning and deep learning models
- Run a data science workflow end to end on messy real data
- Engineer LLM applications with evaluation and cost control
- Build RAG systems over large document collections
- Design agentic systems with tools, memory, planning and guardrails
- Deploy, monitor and retrain models with MLOps practice
The four systems that anchor your portfolio
Substantial builds that go beyond module exercises — designed, documented and defended.
An end-to-end ML system with a deployed API
A production RAG assistant over a large corpus
A multi-agent workflow with guardrails and full tracing
An MLOps pipeline with monitoring and automated retraining
Everything in one programme
Specific libraries and models change every year, so the programme is built around the parts that transfer — retrieval design, evaluation, guardrails, cost control and deployment.
Programme lead
Lead Mentor — AI Engineering
AI Solutions Architect
Builds production LLM and RAG systems for clients and brings that work directly into the classroom, including the failures that never appear in tutorials.
- LLM Engineering
- RAG
- Prompt Engineering
- Vector Databases
- Evaluation
Mentor profiles are placeholders describing the role. Named profiles and credentials will be published before launch.
Why most AI prototypes fail in production
A free introductory session covering the four failure modes we see most often — ungrounded answers, no evaluation, no guardrails and unmanaged cost — and how each is engineered away.
Video hosting is being finalised. Request access below and we will send you the session link.
Before you commit a year
The questions worth asking about a programme this long, answered plainly.
No. The programme teaches the mathematics you actually need, when you need it, in the context of a problem you are solving. If you are comfortable with school-level algebra and willing to work through the material, you can complete this path.
Yes — most learners do. Live sessions are scheduled outside standard working hours and every session is recorded. Plan for roughly 8 to 12 hours per week to keep pace with the project work.
AI Developer teaches you to build applications on top of existing models, and takes 4 to 5 months. AI Pro Mastery covers that plus machine learning, deep learning, data science and MLOps, so you can also build, evaluate and operate models yourself.
Basic programming familiarity in any language helps, but the Python module starts from fundamentals. Complete beginners are accepted and are given additional foundation sessions in the first few weeks.
Yes. Sixteen module projects plus a substantial final system, all built on real data with documentation and evaluation. That portfolio is what you take into interviews.
Specific models change constantly, so the programme is built around the parts that transfer: retrieval design, evaluation, guardrails, cost control and deployment. Model-specific material is refreshed each cohort.
Join AI Pro Mastery
Tell us your background and what you are aiming for. We will send the next batch schedule, the full syllabus pack, fee details and the free demo lesson — and tell you honestly if a shorter path would serve you better first.
Ready to Build Your Future with AI?
Sixteen modules, sixteen projects, live mentorship and a portfolio you can defend in any technical interview.