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Software Development

Design a data estate against carbon, energy, water and waste — from sourcing through to disposal.Most code is now written with AI. Learn what makes software green, and apply it when you write, generate or review.

The developer's job has shifted. You describe what you want, a tool produces it, and you decide whether to accept it — often faster than you can read it. That only works if you can tell efficient code from wasteful code. Fifteen modules on what makes software green, applied to the three things you now do: write it, generate it, review it.
18h
of learning
15
modules
30 question
final assessment
80%
to pass
Credential
on completion

Green Software Developer

Why
How
Tools
Who

How the course works

One lifecycle, one expression

SCI runs through all thirteen modules, with E split into stored, moved and scanned, I as the carbon-aware term, M as media and servers, and R as the functional unit you choose.

A design gate at every stage

Each of Modules 3 to 11 ends with the question to require before sign-off, and Module 12 assembles the nine into a review artefact.

Patterns with the trade stated

Around fifty catalog entries, each naming the term it moves and whether it saves once or on every hour, query or run.

Why take this course

You are approving code you did not write

Generated code follows the average of what it learned from, and that average is full of a query per row, results with no limit, and retries with no ceiling. If you cannot name those patterns, you cannot ask for better or reject what comes back.

Green is not the same as fast

They agree most of the time. Prefetching, retrying twice and keeping capacity warm all make things faster and all add work. The course covers the six cases where they disagree and how to decide.

Nothing tells you when you get it wrong

A slow service pages someone. A wasteful one just runs. There is no alert, so the only way to catch it is to measure it and put a check in the pipeline.

Who this is for

  • Developers writing production code, in any language

  • Anyone generating a large share of their code with AI tools

  • Reviewers approving more code than they could have written

  • Tech leads deciding what the team's defaults should be

No prior sustainability knowledge and no other GSF course needed.

Related courses:

  • Effective, Efficient and Green AI Prompting — the prompts and agents behind your AI features
  • Building a Green AI Agent — putting this guidance inside your coding tools
  • Green and Efficient Architect — the decisions above the code
  • GreenOps Practitioner — running it day to day
  • SCI for AI Practitioner — producing a figure you can defend

What you will work with

A runnable lab, if you want it

A working service with ten deliberate defects. Measure it, fix them, measure again: 301 queries become 2, 140 polls become 37. Node only, no setup, no keys. Optional and not part of the credential.

Things you keep

A one-page way of describing what a feature costs before you build it, a record of the changes you made and what each cost you, and a worked example on one path of your own code.

Measurement you can do today

You probably have no way to measure energy. The course uses what you do have — queries, bytes, calls, tokens — and is clear about what those numbers can and cannot prove.

What you will be able to do

  • Tell efficient code from wasteful code, whether you wrote it or a tool did
  • Decide in ten minutes whether a feature is worth optimising at all
  • Spot the usual defects on sight: a call in a loop, a query with no limit, a response returning fields nobody reads
  • Write an instruction that gets efficient code back the first time
  • Measure what an AI feature costs across many requests, and cap the expensive ones
  • Make a feature do less work when load is high or a budget is running out
  • Find the requests nobody is waiting for, and stop the ones that earn nothing
  • Retire dead code and dormant features without breaking someone's job
  • Prove a change worked, and add a check so it stays that way
  • Set defaults so the next person gets it right without knowing any of this

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

  • Five questions after each module, drawn at random
  • A final assessment of 30 questions from a pool of 210
  • Every attempt covers all 14 required modules
  • 80% to pass, 60 minutes, retake available
  • Questions describe a situation and ask what you would do

What you earn

GSF Certified
Green Software Developer
  • Issued on passing the final assessment
  • Valid for 24 months
  • Shareable for your profile, CV and tender responses
  • The optional lab does not affect it either way

Time to complete

Reading
11h 5m across the 14 required modules
Exercises on your own code
About 7 hours
Final
assessment
60 minutes
Typical completion
18 hours, or about 21 with the lab — entirely self-paced
Optional lab
40 minutes reading, plus 2–3 hours of hands-on work

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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