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Understand where a model’s energy, carbon and water footprint is decided, from dataset to retirement.
Ten modules on the training side: six routes to a working model, sizing and data efficiency, the training run, search and evaluation, hardware-aware training, when and where to run, compression, and lifecycle decisions — each amortised over the model’s serving life.
7h 30m
10
30 questions
80%
Credential
Green AI for ML Practitioners
How the course works
One equation, ten modules
(E × I + M) ÷ R, with each module moving one term and amortisation deciding which move is worth it.
Patterns rather than tips
About twenty catalog entries, each stating the term it moves and whether it saves once or on every inference.
The arithmetic is shown
Break-even volumes and payback periods, including the cases where a technique does not repay.
What this course helps with
Know which decision moves which term
One expression runs through the course, and each module changes one part of it, so the choices are comparable.
Compare a one-off saving with a recurring one
Amortisation over expected serving volume, which is what decides whether a reduction is worth making.
Count the work that is usually missed
Search, evaluation and synthetic generation frequently exceed the training run itself and are rarely in the figure.
Who this is for
- ML engineers, applied scientists and research engineers
- Data scientists who train, fine-tune and retrain models
- ML platform teams setting defaults for others
- Prerequisite: SCI for AI Practitioner, or the ability to produce a measured figure
Related courses:
Assumes you can already produce a measured figure. SCI for AI Practitioner covers that first.
What you will work with
The six routes
Reuse, retrieve, merge, adapt, fine-tune or train, with the calculation that ranks them for your volume.
The multiplier chapter
Search and evaluation budgets, where much of the training-side compute actually goes.
Placement
Time- and region-shifting, and why an intensity reduction is reported differently from an efficiency gain.
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 certificate
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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