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Water

Read a software water number, know what it covers, and know when it cannot be compared.

Six short modules in plain language: why water reaches every software role, the four layers of software water, consumption versus withdrawal, how to read a water number and state what it leaves out, why a litre in one place is not a litre in another, and the small things each role can do. No prerequisites and no calculation required.
2h 50m
of learning
6
modules
12 question
final assessment
80%
to pass
Credential
on completion

Software Water Foundations

What
How
Tools
Who

How the course works

Basic on purpose

Plain language throughout, no opinions, no arithmetic to perform. The outcome is to recognise, interpret and question a water number — not to calculate a full SWI.

Draft status stated everywhere

The Software Water Intensity specification is draft v0.1.0, a work in progress and subject to change. Every module says so where it cites the spec.

One worked example, carried through

Northbridge, the same constructed organisation used across the catalogue, so the figures stay consistent from module to module.

What this course helps with

The dimension most teams have not started on

Carbon has a ratified standard and years of practice behind it. Water is newer, the specification is still in draft, and most software teams have never been asked the question. This course is the entry point.

Numbers that arrive without a basis

One service reports 12 mL per request, another 9 mL. Module 4 walks that comparison and reaches the honest answer: not comparable as stated, and here is exactly why.

A litre is not a litre

The same volume means something different in a water-stressed basin. Module 5 explains stress adjustment, and why the AWARE and Aqueduct scales cannot be swapped for one another.

Who this is for

  • Every software role — developers, testers, architects, data and platform engineers, product and delivery

  • Sustainability practitioners adding water to work that so far covers only carbon

  • Anyone who has been handed a water figure and asked whether it can be used

  • No prerequisites and no engineering seniority assumed

Related courses:

This is a cross-role feeder course. For producing footprint figures rather than reading them, see SCI for AI Practitioner: The Measurement Lab. For the placement and capacity decisions that change a water number, see Green and Efficient Architect and GreenOps Practitioner.

What you will work with

The four layers

Building, electricity, hardware and the stress conversion — the structure everything else in the course hangs from.

The Northbridge worked entry

1,000 kWh in a month at a WUE of 0.3 L/kWh gives 300 L direct; a grid factor of 1.5 L/kWh gives 1,500 L indirect; across 100,000 requests that is roughly 18 mL per request. Illustrative figures — and quantified operational water only, embodied not yet quantified, so a partial result rather than a complete SWI.

The “can you trust this number?” card

A short exercise on two published figures, and what has to be true before they can sit in the same sentence.

What you will be able to do

  • Explain why software water matters to your role, whatever that role is
  • Name the four layers — direct, indirect, embodied and the stress conversion — and say which layer a number covers
  • Tell consumption apart from withdrawal, and use the right word
  • Read a water number and state plainly what it includes and what it omits
  • Explain stress-adjusted water, and why two stress scales are not interchangeable
  • Pick the small actions available to you and say what they do and do not achieve

Course Lessons

The full course

Every module ends with an application exercise and a worked solution.

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

How you are assessed

  • A five-question knowledge check after every module, drawn at random from that module’s 15-question pool
  • A final assessment of 12 questions — two from each module, from a pool of 90
  • 80% to pass, 30 minutes, retake available after 30 days
  • Questions test interpretation and judgement, not recall of figures that date

What you earn

GSF Certified
Software Water Essentials
  • Issued on passing the final assessment
  • Valid for 24 months, refreshed by a short update module
  • Shareable credential for your profile, CV and tender responses

Time to complete

Reading and exercises
2h 50m across 6 modules
Knowledge checks
About 5 minutes per module
Final assessment
30 minutes
Typical completion
1–2 weeks at a module per session, entirely self-paced

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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