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Learn to build with AI — not just talk about it.

CapabilityNext is a hands-on training institute for technical students and working engineers. You ship real projects every week, mentored by people who do this for a living — one click from your browser, on GPU infrastructure we own.

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1‑clickbrowser lab, our GPUs

4specialisation tracks

90%project-based hours

1:8mentor-to-student ratio

rag_pipeline.py

# Week 3 · build a retrieval-augmented assistant
from capabilitynext import Retriever, LLM

docs   = Retriever.from_dir("./handbook")
model  = LLM("claude-sonnet", temperature=0.2)

def answer(question):
    ctx = docs.search(question, k=5)
    return model.ask(question, context=ctx)

# ship it, measure it, iterate
answer("How do I request study leave?")

Learn something in 30 seconds

This is how a model learns.

Every model you will train, from a small classifier to a large language model, improves by the same loop: measure the error, then step downhill. Drag the learning rate and see what happens when the step is too small, just right, or too big.

A taste of week 1 in Introduction to Machine Learning. The loss here is f(w) = (w − 2)²; the update is w ← w − lr × 2(w − 2).

Learning rate 0.10

Your lab, in the browser

Train on our GPUs — from any device you already own

CapabilityNext runs on high-end GPU infrastructure we own and operate. No workstation to buy, no CUDA drivers, no dependency hell — you open a browser and start learning.

One-click deployment

Every course opens a ready-to-run workspace — notebooks, datasets, frameworks and a GPU already wired up. Nothing to install.

Any device

Phone, tablet, Chromebook or an old laptop. The heavy compute is ours; you just need a screen and a connection.

Zero setup

No package installs, no version conflicts, no “works on my machine”. Your time goes into learning, not configuring.

Real GPU training

Train and fine-tune real models on real accelerators during live sessions — not toy examples on a CPU.

One-Click Deployment — how it works

Open your course

Pick this week's module. No downloads, no accounts to wire up.

Week 3 · Retrieval-augmented generation ▶ Launch lab

Click “Launch lab”

We spin up a workspace on our GPUs with the datasets and libraries pinned.

Provisioning workspace… Attaching GPU · loading datasets ready in ~15s

Start building

A notebook opens with everything imported. Same setup for every student, every device.

$ nvidia-smi GPU 0 · 40GB · available >>> from capabilitynext import lab ✓

How the platform works →

Specialisation tracks

Four routes into a career in applied AI

Start with foundations, then go deep in the track that matches the role you want. Every track is capstone-driven and portfolio-ready.

Every star is a skill you can learn here. Click one to see which course teaches it, and light up the sky.

See the full catalogue

🎓 Ways to learn

Four ways to attend — you choose

Same curriculum and GPU lab in every format. Pick the schedule, the amount of live contact and the setting that fit your life; pricing scales to match.

Compare formats & pricing

Featured courses

Popular right now

GenAI & LLM

LLM Application Engineering

Design, build, evaluate and ship an LLM product: retrieval, tool use, agents, guardrails and cost control.

⏱ 10 weeks🖥 Part-time🧩 3 projects

IntermediateDetails →

Foundations

Python & Maths for Machine Learning

From NumPy to gradient descent. Build the intuition and the toolkit every AI engineer relies on daily.

⏱ 8 weeks🖥 Part-time🧩 5 labs

BeginnerDetails →

Data & MLOps

MLOps on the Cloud

Package, deploy, monitor and retrain models with pipelines, containers and CI/CD you can reuse at work.

⏱ 9 weeks🖥 Part-time🧩 Capstone

AdvancedDetails →

Why CapabilityNext

You learn by building, alongside people who've done it

Project every week

You leave each module with something that runs — code, a model, a deployment — reviewed by a mentor.

Practitioner mentors

Small groups, live reviews, and office hours with engineers shipping AI systems in industry.

Job-ready portfolio

Graduate with 3–5 documented projects, a capstone, and interview prep tuned to AI roles.

Fits around your job

Evening and weekend cohorts, recorded sessions, and a pace designed for working students.

Modern stack

Current tools: PyTorch, Hugging Face, vector databases, LangChain-style orchestration, cloud GPUs.

Learn responsibly

Evaluation, safety, bias and cost are part of every build — not an afterthought.

Read our teaching approach →

How it works

From application to a job you can apply for

Four phases, one part-time programme — and a cohort so you're never doing it alone.

Apply & assess

A short form and a 20-minute call to place you in the right track and cohort.

Foundations sprint

Two weeks levelling everyone on Python, tooling and workflow before the track begins.

Build on real work

Live sessions, guided labs, and a mentor-reviewed contribution to an actual project every module.

Capstone & apply

Ship your capstone, walk away with a portfolio and reference, and start applying to the role your programme maps to.

See the full journey →

🚀 Outcomes

A portfolio that translates into a job

You don't build throwaway practice projects. You contribute to real, in-progress work — so what you graduate with is evidence you can do the job on day one. See what each role pays — and the cost to get there →

Real project work

Genuine projects with real requirements, data and constraints — reviewed like production work, not marked like homework.

A portfolio that translates

Documented, demonstrable work that maps directly to what hiring teams ask about in interviews.

A role on completion

Each programme is built around a live hiring need. Finish the capstone and you can start applying straight away. Salaries by role →

A sample of what graduates ship

Every capstone is a real project on a real brief. Here's the kind of work that comes out of a cohort. (Illustrative examples — real cohort projects to be added.)

GenAI & LLM

Policy assistant with source citations

RAG assistant over a 400-page handbook; 92% answer accuracy on a 50-question test set, under a fixed cost per query.

Data & MLOps

Churn model, deployed and monitored

End-to-end pipeline: feature store, containerised serving, drift alerts and an automated retraining trigger.

Computer Vision

Document layout extractor

Detection + OCR pipeline turning scanned invoices into structured JSON, evaluated on a held-out set.

120+hours of guided build time per track

4tracks from beginner to advanced

1:8mentor-to-student ratio

100%online, evening & weekend friendly

Mentors

Taught by people who ship

Our mentors work in ML engineering, data platforms and applied research. They bring current problems into the classroom.

A. Krishnan

ML Engineer · Recommender systems

R. Mehta

LLM Engineer · RAG & agents

S. Nair

Data Platform Lead · MLOps

P. Thomas

Applied Researcher · Computer vision

💬 Student stories

What graduates say

"I came in writing scripts and left having deployed a monitored model on the cloud. The weekly reviews are what made it stick."

DKDivya K.Data Engineering & MLOps track

"The LLM track was exactly the practical grounding I needed. My capstone became a demo I now show in interviews."

ASArjun S.GenAI & LLM Engineering track

"As a final-year student this bridged the gap between coursework and what teams actually expect. Worth every evening."

MFMariam F.AI/ML Foundations track

🤝 For institutions

Run CapabilityNext at your campus

Colleges, training institutes and edtech platforms partner with us to deliver job-focused AI programmes — on-site, online, or as a franchise. We bring the curriculum, the GPU platform and faculty training; you bring the learners.

Deliver it yourself

Run cohorts at your facility with your faculty — we license the curriculum, host the lab and train your trainers.

Promote online only

Refer learners into our virtual cohorts, co-branded, and earn a revenue share — no facility or faculty needed.

Franchise a territory

Exclusive rights to deliver our programmes across a region, with the full playbook and support.

Become an education partner