Write your awesome label here.
Start a green software and green AI programme, and grow it beyond the first team.
An adoption journey in seven steps: build awareness, choose a first portfolio, set a baseline, apply patterns, embed checks in delivery, run reduction campaigns, and keep it going with a dashboard and a governance rhythm.
5h 15m
7
30 question
80%
Credential
Getting Started and Scaling Green Software and Green AI
How the course works
One organisation throughout
Northbridge appears in every module — the same estate and the same numbers — so the steps connect into a single journey.
Patterns from a curated catalog
Each reduction has a precondition, a trade-off and a verification step, and is applied through a select–apply–verify loop.
A workbook you fill in
Seven steps that assemble into an adoption plan for your own organisation, each with a worked solution to check against.
What this course helps with
Awareness that holds
Understand the four dimensions — carbon, energy, water and waste — and where they are created across the estate, so the conversation starts from facts.
A first step that is small enough to take
A portfolio of a few workloads chosen on materiality, tractability and evidence, rather than a programme that tries to cover everything.
Work that continues after the launch
A cadence, an owner and a dashboard, so the programme does not depend on one person’s attention.
Who this is for
- Anyone starting or scaling a green programme, in any role
- Product, design, engineering, ML and data, platform and infrastructure teams
- Procurement and governance colleagues joining the effort
- No sustainability background required; Modules 3–6 assume familiarity with delivery tooling
Related courses:
Already measuring confidently and want depth on one layer? The practitioner courses go further on measurement, architecture and operations.
What you will work with
The portfolio test
Three questions — material, tractable, evidenced — for choosing the first workloads, and what to say to teams not in the first cohort.
A completed baseline row
Unit of work, rate and total, boundary, data tier and date, filled in for one service.
The campaign design
Scope, baseline, quality bar and the go/no-go stated before the work begins.
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.
Thank you.
We will be in touch shortly
We will be in touch shortly