— AI certification for ML engineers —

Certification for machine learning engineers,
proven in production.

Training a model is the part everyone tests. Shipping it, and keeping it alive when the data shifts, is the part that separates engineers. The Institute of AI is the UK's professional body for AI, and IoAI accreditation assesses the whole production lifecycle against a published competency framework: proof you can run systems, not just train them.

Find your level, free See the framework

— The whole board, not one column —

Vendor exams check one lane.
We assess the board.

This is the work of getting a machine learning model into production and keeping it there. A vendor certification proves you can operate the build column. IoAI accreditation asks whether you can move a card the length of the board and answer for it.

Frame the problem

Decide whether ML is the right tool at all
Define success, and the guardrails to stop
Source, check and document the data

Build and train

Set a baseline and choose the model
Train, tune and track the experiments
Version the data, code and weights

Where vendor certs stop

Evaluate honestly

Test beyond the happy path
Measure bias and failure modes
Set an evidenced bar to ship

Ship to production

Package and serve the model
Automate the release, with a rollback
Prove it holds under real load

Operate and monitor

Watch for drift and degradation
Alert before users feel it
Own the incident when it breaks

5/5IoAI accreditation assesses the whole board, at the level you apply for. Training the model is a single column of the job.

— The production edge —

Run systems, not
just train them.

A model that scores well in a notebook has proven almost nothing. The value, and the risk, lives in production. That is exactly where IoAI accreditation concentrates.

See the framework

MLOps, not notebooks

Packaging, serving, versioning and automated release. The assessment looks at how a model actually reaches users, and how you get it back if the release goes wrong.

Evaluation you can defend

Anyone can quote an accuracy figure from a demo. IoAI accreditation probes how you test beyond the happy path, measure failure modes and decide, on evidence, that a model is fit to ship.

Monitoring and accountability

Models drift, data shifts and systems fail quietly. We assess how you watch for it, alert on degradation, and stay accountable for the decision under UK expectations, including the ICO.

— One credential, four levels —

A ladder for a career,
not a single exam.

You apply at the level your current practice supports, and progress upward as it grows. Professional is the grade for ML engineers shipping to production and accountable for what they ship.

Explore accreditation

01
Student
Studying or new to the field. Get the fundamentals right before habits set.
02
Associate
Applying ML in the day job, safely and accountably.
03
Professional
Independent practice
For ML engineers shipping to production and accountable for what they ship.
04
Fellow
Senior engineers shaping how teams build, review and operate ML.

Find your level, free Apply for accreditation

— Why the standard holds —

A standard is only worth
what stands behind it.

The Institute of AI is the UK's professional body for AI, and setting the standard for the profession is its remit. The framework is published, the assessment is judged against it, and the result is verifiable by anyone.

Four: levels, Student to Fellow, on one published competency framework
One: professional body setting the standard, independent of any tool vendor
Annual: CPD targets by level, logged and evidenced
Public: verification any employer can check

The framework, the levels and the CPD targets are all published in full.

— Common questions —

Before you apply.

How is this different from an MLOps or cloud ML certification?+

A cloud certification proves you can operate one platform, which is genuinely useful. Keep it. IoAI accreditation sits above the stack: it assesses whether you can get a model into production and keep it there responsibly, whatever tools you use. The two answer different questions.

Which level should a machine learning engineer start at?+

Professional is the grade for ML engineers shipping to production and accountable for what they ship. You apply at the level your current practice supports, and progress upward from there. The competency framework sets out exactly what each level expects across the lifecycle.

Do I need to reveal proprietary code or production data?+

No. The assessment is evidence-based against the framework, not a code dump. You demonstrate how you frame, build, evaluate, ship and operate ML systems, described at the level you are applying for, without exposing anything your employer would not want shared.

What does it cost?+

A one-off assessment fee of £29.99, then annual membership by level: Student £49.99, Associate £99.99, Professional £199.99, Fellow £299.99. It is held through continuing professional development, not re-sat every few years.

Why should this credential carry weight with an employer?+

Because the Institute of AI is the UK's professional body for AI, and it sets the standard for the profession rather than for any one supplier. The competency framework is published before you apply, the level is assessed against it, and an employer can verify the result publicly rather than take it on trust.

Proven in production

Prove you can ship ML, and keep it running.
Get accredited.

Sit an evidence-based assessment with the UK's professional body for AI and earn a credential that recognises the whole lifecycle, not one column of it.

Apply for accreditation See the framework

— Related pages

— Discipline