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Measure a real AI workload end to end, and record every number so it can be checked.
A hands-on measurement lab covering SCI and SCI for AI, four measurement approaches — research estimates, provider data, your own model, third-party tools — plus direct measurement, intensity, embodied impact, boundaries and reporting.
12h 25m
14
30 question
80%
Credential
SCI for AI Practitioner: The Measurement Lab
How the course works
A lab, not a lecture
You measure a real workload end to end, then compare the approaches against each other.
Evidence recorded as you go
Method, level, boundary and date on every number, which is what makes a figure defensible later.
Built on the ratified specification
SCI (ISO/IEC 21031) and SCI for AI, cited precisely, with pathway status stated.
What this course helps with
Answer the second question
"What does it include?" is where most footprint figures come apart. The boundary discipline is the core of the course.
Interpret provider data
What a vendor figure covers, what it omits, and how to use it without overstating what it says.
Explain why tools disagree
Four approaches, different results, and a way to reconcile and explain the gap.
Who this is for
- Engineers, architects and sustainability practitioners producing AI footprint figures
- Practitioners preparing figures for disclosure
- Anyone answering customer, audit or regulatory questions about AI impact
Related courses:
To govern measurements rather than produce them, Green AI for Executives covers the leadership view.
What you will work with
One score, four inputs
The SCI structure applied to AI, with the functional unit treated as the decision it is.
The reconciliation
Why four approaches disagree, and how to explain the difference.
Boundaries and the register
What is in, what is out, and where it is recorded for the next person.
What you will be able to do
Course Lessons
The full course
01
Where ML’s Footprint Is Decided
A model’s footprint is created twice: once when it is trained, and again on every inference it serves. This module sets the decision metric and the amortisation rule that every later module is judged against.
module-01
02
Reuse, Adapt, or Train: The First Decision
The largest reduction available on the training side is not to train, yet the question is usually settled by habit before anyone computes it. This module compares six routes to a working model and what to ask of a base model before inheriting its cost.
module-02
03
Sizing the Model and the Data Budget
How large a model, trained on how much data, sets the energy term more directly than any later choice. This module covers compute-optimal sizing and how architecture changes training and serving energy independently of parameter count.
module-03
04
Data Efficiency: What the Model Is Trained On
What the training tokens actually are changes both how many are needed and how good the resulting model is. This is the layer where the footprint lever and the quality lever most often point in the same direction, and this module is careful about where that alignment breaks.
module-04
05
The Training Run: Execution, Stopping and Waste
This module examines the compute that produces nothing: epochs past convergence, crashes without a checkpoint, and experiments that repeat work already done. Waste at this layer is invisible in a cost report, because a failed run and a successful one look identical on the bill.
module-05
06
Search and Evaluation: The Two Multipliers
Earlier modules reduce the cost of one run; this one addresses how many runs happen, which is usually the larger number. It covers search budgets, comparing strategies by cost per answer, and evaluation as a cost that recurs for the life of the model.
module-06
07
Hardware-Aware Training
Compute demanded is one quantity; how efficiently it becomes energy is another. This module covers precision, batch size, accelerator fit and utilisation as the conversion factor sitting between the two.
module-07
08
When and Where to Train
This is the one lever that changes carbon without changing energy, by moving a run to a different hour or region. Training is often a strong candidate, and this module covers the constraints that decide it, along with water, preemptible capacity and embodied allocation.
module-08
09
From Weights to Deployable Artifact
This module deliberately spends training cost once in order to reduce serving cost on every inference that follows. It covers parameter-efficient adaptation, post-training and compression, and the repayment calculation that decides whether the trade is worth making.
module-09
10
Lifecycle, Retirement and Disclosure
Retraining cadence multiplies the training footprint, and retirement is the point at which every amortisation estimate becomes a measured fact. This closing module covers cadence, drift, registry and model card records, disclosure duties, and the capstone footprint report.
module-10
11
Final Assessment
A final assessment of 30 scenario-judgement questions - 80% to pass, 60 minutes, retake available.
Assessment and Credential
Time to complete
Join the Pilot
The GSF Academy is currently in pilot. If your organization would like to enrol learners and issue GSF certifications to your teams, tell us a little about what you need and we'll follow up to discuss access, pricing, and enrolment.
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