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Data & AI · GAI-01

Certify what you can build, judge and answer for in AI

The Artificial Intelligence standard recognises people who can take a model from problem framing to production and stay accountable for what it does once it is there. It is assessed on shipped work, not on recall of architectures.

24,762 holders worldwide 6 levels +9.6% growth this year
What this standard certifies

Six capability areas, assessed on evidence

Each area is evidenced from your own work. Assessors look for what you decided and why, not for a rehearsed answer.

Problem framing
Deciding what should and should not be solved with a model in the first place.
Data readiness
Sourcing, labelling and documenting data, including what its gaps mean for the result.
Evaluation
Designing tests that reflect real use, not just a favourable benchmark.
Deployment
Serving, monitoring and retraining models under production constraints.
Governance & ethics
Bias, explainability, consent and the regulatory line the work has to stay behind.
Human oversight
Building the escalation path for when the model is wrong, because it will be.
Levels in this standard

Artificial Intelligence across the recognition ladder

The same six-level framework, expressed in the terms of this discipline. Experienced applicants can enter directly at a higher level through the portfolio route.

Level Post-nominal What it certifies Professional Experience Annual CPD
1BIPS Foundation BIPS-F Understands model families, data requirements and the limits of automated decisions. 0–1 yr 10 h
2BIPS Practitioner BIPS-P Trains and evaluates models on defined problems, with a senior reviewer on the work. 1–3 yrs 15 h
3BIPS Professional BIPS-Pro Owns an AI feature end to end and is accountable for its behaviour in production. 3–6 yrs 20 h
4BIPS Specialist BIPS-S Leads a specialism — safety, MLOps, applied research — with peer-reviewed evidence. 6–10 yrs 25 h
5BIPS Expert BIPS-E Sets AI direction across an organisation and is cited beyond one employer. 10+ yrs 30 h
6BIPS Fellow FBIPS Shapes the profession itself through standards, public work or sustained contribution. By election 30 h
Who it is for

Built for people already doing the work

Machine learning engineers Data scientists moving into production Applied researchers Product leads owning AI features Risk and governance specialists Engineers retraining into AI
How you are assessed

Four stages, no written exam

1

Portfolio

You submit real work against the six capability areas.

2

Evidence review

An assessor checks provenance and your role in it.

3

Professional discussion

A recorded conversation about decisions and trade-offs.

4

Award & register

Your credential is issued and listed publicly.

Where it leads

Roles this standard opens up

Machine Learning Engineer

Builds and runs the models that carry live decisions.

AI Product Lead

Decides what gets automated, and what deliberately does not.

AI Governance Specialist

Holds the line on bias, explainability and regulation.

Head of Applied AI

Sets direction and carries organisational accountability.

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