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Machine Learning (ML)

Understand where a model’s energy, carbon and water footprint is decided, from dataset to retirement.

Ten modules on the training side: six routes to a working model, sizing and data efficiency, the training run, search and evaluation, hardware-aware training, when and where to run, compression, and lifecycle decisions, each amortized over the model’s serving life.
7h 30m
of learning
10
modules
30 questions
final assessment
80%
to pass
Certification
on completion

Green AI for ML Practitioners

What
How
Tools
Who

How the course works

One equation, ten modules

(E × I + M) ÷ R, with each module naming the term or multiplier it moves and amortization deciding which move is worth it.

Patterns rather than tips

More than thirty catalog entries, each stating the term it moves and whether it saves once or on every inference.

The arithmetic is shown

Break-even volumes and payback periods, including the cases where a technique does not repay.

What this course helps with

Know which decision moves which term

One expression runs through the course, and each module names the term or multiplier it changes, so the choices are comparable.

Compare a one-off saving with a recurring one

Amortization over expected serving volume, which is what decides whether a reduction is worth making.

Count the work that is usually missed

Search, evaluation and synthetic generation frequently exceed the training run itself and are rarely in the figure.

Who this is for

  • ML engineers, applied scientists and research engineers

  • Data scientists who train, fine-tune and retrain models

  • ML platform teams setting defaults for others

  • Prerequisite: SCI for AI Practitioner, or the ability to produce a measured figure

Related courses:

Assumes you can already produce a measured figure. SCI for AI Practitioner covers that first.

What you will work with

The six routes

Reuse, retrieve, merge, adapt, fine-tune or train, with the calculation that ranks them for your volume.

The multiplier chapter

Search and evaluation budgets, where much of the training-side compute actually goes.

Placement

Time- and region-shifting, and why an intensity reduction is reported differently from an efficiency gain.

What you will be able to do

  • Separate a model's training footprint from its serving footprint, and spread the training cost across its serving life
  • Choose among six routes to a working model based on the numbers
  • Size the model and its training data for the model's serving life
  • Set budgets for training runs, search and evaluation before their cost grows
  • Shift training to lower-carbon times and regions, and report the result as an intensity reduction
  • Decide on compression and retraining cadence based on payback and measured drift

Course Lessons

The full course

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

01

The Prompt Footprint

Introduces the five-dimensional view: cost, carbon, energy, water, embodied hardware. Establishes SCI for AI as the standards anchor. Positions the regulatory landscape (CSRD, EU AI Act, procurement disclosure) as the why now for Footprint literacy.
Before You Begin
Modules

02

The Specification Lever

How prompt intent quality drives Footprint. The GSF Intent Framework introduced. Trade-offs: over-specification wastes tokens, under-specification wastes retries. Worked examples across capability tiers.
Modules

03

The Structure Lever

Prompt organization: system messages, few-shot examples, output constraints, formatting instructions. Where structure earns its tokens and where it doesn't.
Modules

04

The Reasoning Depth Lever

When to invoke chain-of-thought, tree-of-thought, extended reasoning modes; when not to. The GSF Reasoning Budget: task-class taxonomy for reasoning depth decisions.
Modules

05

The Model Selection Lever

The most consequential single decision in inference efficiency. Capability tiers and routing decisions. The GSF Model Fit Test protocol. Same task across tiers, measured.
Halfway Check-in
Modules

06

The Orchestration Lever

Where agents leak resource: tool selection, chain depth, redundant retrieval, missing caching. Token budgets as a first-class design constraint. Case study from the supply-chain agent paper (70–90% workflow-level savings).
Modules

07

Measuring the Prompt Footprint

Hands-on demonstrations with EcoLogits, CodeCarbon, AI Energy Score. Producing numbers a CSRD auditor can read. The measurement chain from token count to gCO₂eq.
Modules

08

Procurement & Disclosure

What to ask your LLM vendor. What CSRD and the EU AI Act will require. The GSF Procurement question bank organized by Footprint dimension.
Modules

09

Course Completion

50 questions, 120 minutes. Pass Marks: 85%
Final Assessment
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Assessment & Certification

How you are assessed

  • A short knowledge check after every module, drawn at random from that module’s pool
  • A final assessment of 30 scenario-judgement questions from a pool of 150
  • 80% to pass, 60 minutes, retake available
  • Questions test judgement in situations you will meet — not recall of figures that date

What you earn

GSF Certification
Green AI Practitioner
  • Issued on passing the final assessment
  • Valid for 2 years, refreshed by a short update module
  • Shareable credential for your profile, CV and tender responses

Time to complete

Reading and exercises
7h 30m across 10 modules
Knowledge checks
About 5 minutes per module
Final assessment
60 minutes
Typical completion
2–3 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 what you need, and we'll follow up to discuss access, pricing, and enrolment.

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