Upskill. Lead.
Build your own product.
Upskill in AI. Lead teams. Launch products. Flex evenings & weekends.
What you leave with
Industry certification
A verifiable certification tied to your delivered portfolio — not just an exam pass. Something tangible you can point to.
Real project delivery
Delivered client work from month 2 onwards. Portfolio-worthy, sometimes paid, always a real brief from a real company.
Technical leadership
Lead engineering teams, own architecture decisions, and drive AI product strategy at the senior level.
Product launch
From idea to first paying customer — with the full-stack and AI skills to build the entire thing yourself.
Your dashboard
Built for professionals
Certification Roadmap
Your AI/ML certification track — completed units, current assessment, upcoming exams, and projected completion date. Every milestone shown clearly.
Industry Projects
Live client briefs from the TechOps engineering team. Apply, get matched, and deliver real work that goes directly on your professional portfolio.
Mentorship Sessions
1:1 sessions with senior engineers and founders, bookable by topic — system design, AI products, leadership, or career transitions. Notes saved to your profile.
Flex Learning Schedule
All sessions run evenings and weekends. Catch-up recordings included. Designed entirely around a full-time job — not the other way around.
Skills Progress Map
Depth scores across AI engineering, system design, technical leadership, and product. See your level — novice, practitioner, or expert — and what each next step requires.
12 months · graduate-grade
The Pro curriculum
Graduate-level rigor — modelled on Stanford CS229, CS230 & CS224N and Full-Stack Deep Learning — but every module ends in something shipped, not an exam. And an AI co-pilot works beside you in every module — explaining concepts, reviewing your code, and drilling you 24/7, alongside human mentors.
- Supervised & unsupervised learning; the bias–variance tradeoff
- Optimisation: gradient descent, regularisation, convergence
- Evaluation that survives production: leakage, drift, calibration
- From notebook to a reproducible training pipeline
- Neural nets, backprop & autograd
- CNNs for vision; sequence models
- Training at scale: schedules & regularisation
- Transfer learning & fine-tuning
- Transformers & attention from first principles
- Retrieval-augmented generation, embeddings & vector DBs
- Fine-tuning, LoRA & alignment (RLHF / DPO)
- Eval harnesses: groundedness, latency, cost
- Tool use & function calling
- Multi-step agents & planning
- Multimodal systems (vision + text)
- Guardrails & agent evaluation
- MLOps: CI/CD for models, versioning, monitoring
- Distributed & efficient inference; cost at scale
- System design for AI products (the senior-interview bar)
- Reliability, observability & on-call discipline
- Eval harnesses & benchmarking
- Red-teaming & adversarial testing
- Bias, privacy & responsible AI
- Monitoring, drift & incident response
- Architecture review & technical decision-making
- Leading, mentoring & hiring engineers
- Stakeholder & executive communication
- Driving AI product strategy
- Idea → MVP → first paying customer
- Pricing, monetisation & unit economics
- Enterprise go-to-market & design partners
- Fundraising fundamentals
Your AI co-pilot
A graduate-level tutor that knows exactly where you are in the curriculum — and never sleeps. It works inside every module, on your real code, so you're never stuck waiting until the next mentor session.
The AI handles the 2am questions. Senior engineers and founders handle the ones that shape a career.
Or $40/month billed annually — 25% off
Employer-sponsored seats available · Cancel anytime
Apply now