Build, Deploy and Scale Production-Ready AI Systems
11 intensive weekends. From Python and LLMs to RAG, AI agents, MLOps and production deployment.
110 hours
Live contact time
6 projects
Plus a production capstone
30 seats
Cohort 1 only
Why this programme
Most AI courses stop at the API call.
Most AI courses teach you to call an LLM API. The gap between that and an AI engineer is everything that comes after the call: retrieval that returns the right document, evaluation that catches a wrong answer, deployment that survives traffic, and a cost model that does not bankrupt the feature. That gap is the entire curriculum.
Build → Test → Evaluate → Deploy → Monitor → Improve. Six times, before the capstone.
Production, not prototypes
Every module ends in something that runs. Build, test, evaluate, deploy, monitor, improve — and you run that loop six times before the capstone.
AI system design as a first-class skill
A full module on architecture: ingestion, SQL versus vector storage, retrieval, agents, queues, caching, auth, observability, evaluation, cost, latency, security, human-in-the-loop.
African business cases, not toy problems
Fintech, logistics, FMCG, customer support and research. The projects look like systems companies here actually pay to have built.
Evaluation taken seriously
Groundedness, retrieval metrics, test sets, hallucination detection, prompt injection and guardrails. Most programmes skip this. It is the difference between a demo and a system anyone trusts.
An employer-facing capstone
Architecture diagram, public repo, deployed URL, README, tests, evaluation report and a five-minute demo — presented at a public Demo Day with employers invited.
Curriculum
Eleven weekends, eleven shipped things.
Saturdays are concepts, architecture and live build-along. Sundays you build, reviewed in the room. Wednesdays are office hours.
01
13 – 14 Feb
AI Engineering Foundations
Python for engineers, Git workflow, environments, FastAPI, testing.
A tested API service, in a repository, running.
02
20 – 21 Feb
ML and Deep Learning Essentials
Data pipelines, supervised learning, metrics, neural-network intuition.
A trained model behind an API endpoint.
03
27 – 28 Feb
LLM Foundations
Transformers and tokens in practical terms, model APIs and selection, prompting patterns, structured outputs, cost and latency as constraints.
A structured-output service with cost instrumentation.
04
6 – 7 Mar
Building LLM Applications
OpenAI, Anthropic and open models. Streaming, conversation state, memory, retries, rate limits, graceful degradation.
Project 1 — Fintech AI analyst over transaction data.
05
13 – 14 Mar
RAG Engineering
Chunking, embeddings, vector databases, hybrid search, reranking, metadata filtering, and making sure the current document wins.
Project 2 — Customer support RAG with citations.
06
20 – 21 Mar
RAG Evaluation and Reliability
Groundedness and faithfulness. Retrieval metrics separated from generation metrics. Test sets, hallucination detection, regression testing.
An evaluation harness and report for your Week 5 system.
07
27 – 28 Mar
AI Agents
Tool calling, function schemas, planning and state, graph orchestration, the Model Context Protocol, and when an agent is the wrong answer.
Project 3 — Logistics agent handling queries and exceptions.
08
3 – 4 Apr
Advanced Agent Systems
Multi-agent patterns and when they earn their complexity. Human-in-the-loop without the bottleneck. Guardrails, approval gates, blast radius, audit trails.
Project 4 — FMCG sales agent with an approval gate.
09
10 – 11 Apr
Open-Source Models and Fine-Tuning
Hugging Face, local inference, LoRA and PEFT, quantisation, serving. When fine-tuning beats prompting and retrieval, and when it does not.
Project 5 — a fine-tuned model serving a narrow task cheaper.
10
17 – 18 Apr
Production AI and MLOps
Docker, CI/CD, cloud deployment, secrets. Observability and tracing for systems that reason. Cost control, caching, rollback.
An earlier project deployed publicly, with monitoring.
11
24 – 25 Apr
AI System Design and Security
Full architecture under constraints. Multi-tenancy, data privacy, prompt injection and defence. Capstone architecture review.
Capstone architecture, reviewed and signed off.
Capstone build period: 21 December – 15 January, with scheduled reviews.
Demo Day is Saturday 22 May 2027 — a public showcase with employers invited. Five minutes, live, to an audience and a technical panel. It is the single most useful thing the programme produces.
Enrol
Choose a tier and tell me where you are.
Pick your tier, tell me what you want to build, and pay by card. Your seat is confirmed the moment the payment clears, and your receipt and welcome pack arrive by email straight away.
Refunds. Full refund up to 48 hours before Weekend 1 on 13 February 2027. After Weekend 1 and before Weekend 2, 50%. From Weekend 2 onward, no refund. Deposits are refundable within these windows.
Before you apply
What you need
- Comfortable writing basic Python: functions, loops, data structures.
- A laptop with at least 8GB RAM and a stable internet connection.
- No prior machine learning, LLM or cloud experience assumed.
- Roughly 6–8 hours a week outside sessions for project work.
Schedule
- Saturday · 10:00 – 14:00 WAT
- Concepts, architecture, live build-along
- Sunday · 14:00 – 18:00 WAT
- Lab — you build, reviewed live
- Wednesday · 18:00 – 20:00 WAT
- Office hours, code review, blockers
Questions
Answered plainly
- Will sessions be recorded?
- Yes, and available during the programme and after. Live attendance is strongly encouraged — the Sunday labs are where most of the learning happens.
- What if I miss a weekend?
- Watch the recording and bring questions to Wednesday office hours. Missing more than two weekends makes the capstone difficult, and we will talk before that becomes a problem.
- Do I need a strong maths background?
- No. You need to be comfortable in Python. The maths is explained where it is needed and skipped where it is not.
- Is there a job guarantee?
- No, and be sceptical of anyone offering one. What you get is a deployed portfolio, a public Demo Day presentation, interview preparation and an alumni network.
- Can my employer pay?
- Yes. Invoices can be issued to a company, and private corporate cohorts are available.
- What happens after the 11 weekends?
- The capstone build period runs to Demo Day on 22 May, with scheduled review sessions. Community access continues afterwards.