Global

Building and reviewing machine learning models responsibly

A ready-made course you customise for your organisation, with a live session deck and two surveys. You only add the 8 details that are specific to your organisation.

See the live deckSee the full outline
Try the coursePreview the survey
Length
45 min
Lessons
6 lessons
Frequency
At onboarding
Last reviewed
21 Oct 2026
Evaluating models honestly
What would you do?

A model predicts "no fraud" every time and scores 99% on data with 1% fraud

Try it: this is one of 14 interactive activities in the course.

What's included

Three pieces that work together

Copy once, get everything. Learners prepare on their own, you answer questions live, and the surveys tell you what stuck.

Self-paced course

Short lessons with real workplace scenarios, not walls of text.

  • 6 lessons, about 45 minutes
  • Process, timeline, tabs, sorting activity and quizzes

Live session deck

An AhaSlides presentation to run after the course, in person or online.

  • 10 slides
  • Quiz, poll, ranking, word cloud slides
  • A closing slide for your contacts

Two surveys

Find out what landed, and whether people use it on the job.

  • Post-course feedback, about 2 minutes
  • 30-day check-in
  • Anonymous by default
  • Results feed your next refresher

Learning objectives

At the end of this course, learners can

Each objective and the lesson that covers it.

IDObjectiveSourceWhere it's covered
TO 345.0Achieve ≥ 80% on the final assessment-Whole course
SO 345.1Split data into training, validation and test sets and explain the purpose of eachBest practiceWhole course
SO 345.2Spot data leakage and evaluate a model with the right measure for the problemBest practiceWhole course
SO 345.3Describe bias, drift and how to monitor for them after releaseNIST AI RMF 1.0 (voluntary)Whole course
SO 345.4Document a model so a reviewer can judge itNIST AI RMF 1.0 (voluntary); US SR 11-7 model risk guidance [check before build]Whole course
SO 345.5Follow your organisation's model review route before deployment + your infoEU AI Act high-risk requirements [check before build]; internal review rulesWhole course

Your part: what a template can't do for you

  • Your workplace details: fill in the 8 highlighted fields so the course describes your own policies, contacts and locations.
  • Records: record completion in your own LMS or record system.

Course outline

6 lessons, about 45 minutes

Minutes per lesson are an estimate, shared out by page count.

ContentInteractiveVideoYou customise
1

A model that scored 99% and failed live

3 pages

9 min
Before you beginNoteList
The scoreTextImage
What went wrongTimelineQuiz
2

Training, validation and test splits

3 pages

8 min
Three setsTabs
Which set?Sorting activity
CheckFlashcardsMatching quiz
3

Evaluating models honestly

3 pages

8 min
Pick the measureTable
The 99% trapScenarioQuiz
CheckMulti-select quiz
4

Bias, drift and documentation

3 pages

8 min
DriftProcess
What to documentChecklistNoteYour info
CheckQuiz
5

Deploying and monitoring

2 pages

6 min
Release gatesChecklistYour info
Raise it earlyProcessYour infoKey fact
6

Review and final assessment

2 pages

6 min
The reviewer's questionsAccordion
PracticeQuizKey factNoteButtonYour info

Live deck and surveys

10 slides to run live, two surveys to close the loop

Pink slides are interactive. Both surveys are anonymous; pink questions are written for this course.

ContentTitle, agenda, "bring a model result you believed
PollHow is your model checked before release?
QuizA model scores 99% on test data but fails live. A likely cause? (Data leakage ✔ · Too small a file · Wrong colour · Slow server)
Content · customiseThree splits and the reviewer questions {{MODEL_REVIEW_ROUTE_LINK}}
RankingRank these checks before release: clean split, leakage check, right measure, documentation, monitoring plan
Quiz1% fraud, model says "no fraud" always. Accuracy is about… (99% ✔ · 1% · 50% · 0%)
Word cloudOne word for a model you stopped trusting
ScaleHow confident are you spotting leakage?
Q&AAsk the trainer anything
Content · customiseClosing {{HELP_CONTACT}}, {{COURSE_URL}}, {{SURVEY_2_URL}}
Post-course feedback: 11 questions, about 2 min
  1. Overall rating 5 stars
  2. Relevant, realistic, right length, helpful activities agree/disagree grid
  3. What was most useful? multi-select
  4. Confidence before this course, and now 1-5 scale
  5. Confidence in splits · leakage · evaluation measures · drift · documentation grid
  6. How is leakage checked today? multiple choice
  7. What model failure would worry you most? short text
  8. I can say ‘this score looks too good’ without feeling judged. 1-5 scale
  9. One thing we should change or add open text
  10. Would you recommend this course? NPS
30-day check-in
  1. Have you used what you learned since the course? multiple choice
  2. How confident are you today? Compare with day 1 1-5 scale
  3. Do you know where to find the model documentation template and review route? yes / no / not sure
  4. A moment where the training helped, or where you weren't sure open text
  5. Which topics should the next refresher focus on? ranking

Results show whether people use the training on the job, not just whether they finished it.

Make it yours

8 fields to fill in, and you're ready

Everything else is done. These are the details only your organisation knows; each one is highlighted inside the course.

  • Link to the model documentation template{{MODEL_DOCUMENTATION_LINK}}
  • Link to the model review and approval route{{MODEL_REVIEW_ROUTE_LINK}}
  • Who to ask for help or to report a concern{{HELP_CONTACT}}
  • Where completion is recorded{{LMS_OR_RECORD_SYSTEM}}
  • Link to the post-course and 30-day survey{{SURVEY_2_URL}}
  • Link to the self-paced course{{COURSE_URL}}
Show all 8 fields
  • List of approved tools and versions{{APPROVED_TOOLS}}
  • Link to the baseline survey{{SURVEY_1_URL}}

FAQ

Common questions

Can I run building and reviewing machine learning models responsibly training online?

Yes. The course is self-paced and online, and the live deck lets you add a live session in the room or online.

How often is this training needed?

At onboarding, and before a model goes through review or into production.

Who needs this training?

Data scientists, ML engineers and the model reviewers who sign off their work; run by data science leads with L&D.

What records should I keep?

Record completion in your own LMS or record system, so you can show who finished the course and when.

Can I edit the content?

Yes, everything. Change the wording, swap scenarios for ones from your workplace, add your logo, or remove parts that don't apply.

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Last reviewed: 21 October 2026Templates are a starting point, not legal advice.

Ready to run it?

Open the live deck to see how the session runs alongside the course and both surveys.

See the live deck