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Run green software and AI as an operational practice, on the cadence you already have.
Carbon, energy, water and waste run as operational signals across the delivery lifecycle and the operating layers: instrumentation, dashboards, an operating rhythm, and reduction work carried out in production and verified.
10h 30m
13
30 question
80%
Credential
GreenOps Practitioner
How the course works
Operational rather than aspirational
Instrumentation, cadence and ownership, in the reviews and pipelines that already exist.
Pairs with FinOps
The same allocation, tagging and meeting, with the environmental columns added alongside cost.
Verification in every practice
The measurement that confirms a change is part of the practice, not an afterthought.
What this course helps with
Numbers that arrive on their own
Instrumentation designed so figures are produced by the system rather than assembled by hand each quarter.
Improvements you can evidence
Every reduction carries the before-and-after measurement that confirms or refutes it.
Signals that reach someone who can act
Ownership for each line, so a dashboard leads to a change rather than a discussion.
Who this is for
- Platform, operations, SRE and DevOps engineers
- Architecture and engineering leads running production estates
- Practitioners adopting GreenOps alongside FinOps
Related courses:
If you are still deciding what to measure, SCI for AI Practitioner comes first.
What you will work with
The operating cadence
What is reviewed weekly, monthly and quarterly, and who owns the response when a figure moves.
Instrumentation that lasts
Telemetry designed to keep producing the numbers as the estate changes.
Reduction in production
Applying patterns where real traffic is, with the guardrails that make it safe.
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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