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Make architecture decisions across the whole estate, with the carbon, energy, water and waste trade-offs visible.
Eleven modules across six layers — hardware and devices, infrastructure, platforms, data foundations, AI workloads, applications — plus governance and observability, and a capstone Green Architecture Blueprint.
8h 15m
11
30 question
80%
Credential
Green and Efficient Architect
How the course works
Six layers, one estate
Silicon to screen, with governance and measurement running across all of them, so AI sits in context.
Worked arithmetic in every exercise
Refresh payback, region placement, cluster utilisation, lifecycle policy, routing and payload — each with a solution to check against.
A capstone that assembles the modules
The Blueprint is built from the artefacts produced in the earlier exercises.
What this course helps with
Know which layer the impact is on
Region, refresh cycle, utilisation, retention, routing and payload sit at different layers, and effort spent on the wrong one returns little.
Name the dimension a decision displaces
New devices reduce in-use energy and add embodied carbon; a cleaner grid may be water-stressed. The course makes the trade explicit.
Produce a blueprint that traces
Every decision linked to a rated hotspot and a measurement row, rather than a list of good practices.
Who this is for
- Solution, enterprise, cloud and platform architects
- Senior engineers moving into architecture roles
- Technical leads responsible for estate-wide decisions
- Architecture experience assumed; no sustainability background required
Related courses:
Pair with SCI for AI Practitioner if you also need to produce the underlying measurements.
What you will work with
The refresh calculation
Embodied cost of replacement against annual in-use saving, and the conditions that override the result.
The region decision
Carbon, water, data gravity, latency and cost weighed together, with the trade stated.
The Blueprint template
Assessment, measurement plan, reduction register and governance wiring, each traceable to an exercise.
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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