Testing ML Apps
Test classic machine learning features: data quality checks, evaluation metrics and quality gates, behaviour, fairness and metamorphic tests, prediction APIs and training-serving skew, ML pipelines and safe model releases, drift monitoring, and a loan-model release review as your final project. A complete, hands-on path from your first test to a production-grade framework.
- 40
- Lessons
- 8
- Sections
- 8
- Practice labs
- ~20h
- Of content
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1def test_segment_recall(df):2 for seg, g in df.groupby('segment'):3 r = recall_score(g.y_true, g.y_pred)4 assert r >= 0.85, f'{seg}: recall {r:.2f}'
The method
Built like real QA work, not a video playlist.
Learn
40 focused lessons, each a few minutes long.
Practice
8 hands-on labs in a browser workspace.
Test yourself
8 section assessments plus a final exam.
Prove it
A public, verifiable achievement for recruiters.
What you will learn
- ML Foundations for Testers: What Machine Learning Is, for Testers, The ML Lifecycle and Where Bugs Enter, The Oracle Problem: Testing Without One Right Answer
- Testing the Data Behind ML: Data Is Part of the Product, Schema, Range and Missing-Value Checks, Label Quality and Class Balance
- Model Evaluation Metrics: The Confusion Matrix, Precision, Recall and F1, Thresholds, ROC and PR Curves
- Testing How a Model Behaves: Behaviour Testing: Beyond Metrics, Invariance and Directional Tests, Metamorphic Testing for ML
- Testing ML Features Inside Apps: How ML Features Sit Inside Apps, Testing Prediction APIs, Training-Serving Skew and Feature Code
- Testing ML Pipelines and Model Releases: ML Pipelines and MLOps for Testers, Unit and Integration Tests for Pipeline Code, Versioning Data, Models and Experiments
Where you will be at the end
- Complete 8 hands-on practice challenges, one per section (from ML Foundations for Testers to Final Project: Testing a Loan Approval Model)
- Pass 8 section assessments
- Build the portfolio project: Project 1
- Pass the Testing ML Apps final assessment and add the result to your Skill Passport
Who this course is for
- Working testers who want to apply it on real projects
- Experienced QA engineers and SDETs going deeper for senior roles
- Anyone preparing for QA interviews that ask about this skill
- Best if you already have: Manual or automation testing experience; Basic Python helps for the labs; no maths beyond percentages
Curriculum
9 sections, one clear path.
- What Machine Learning Is, for Testers
- Knowledge check
- The ML Lifecycle and Where Bugs Enter
- Knowledge check
- The Oracle Problem: Testing Without One Right Answer
- Knowledge check
- Classification, Regression, Ranking and Anomaly Detection
- Knowledge check
- Building an ML Test Strategy
- Knowledge check
- Lab: Write Invariant and Metamorphic Tests for an ETA Model
- Section assessment
Members-only toolkit
Take the course with you
Made from this course's own lessons, quizzes and labs — ready to revise before interviews or use at work.
Course cheat sheet
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Interview Q&A pack
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Lab solutions & hints
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- Auto-checked labs
- Capstone portfolio project
- Timed final exam
Why paid courses
More than a free course: practice, proof and a portfolio
| What you get | Free | Academy |
|---|---|---|
| Short lessons with examples | ||
| Quick checks and certificate | ||
| Hands-on labs checked automatically | ||
| Section tests and a timed final exam | ||
| Portfolio capstone project with milestones | ||
| Cheat sheet, interview pack and lab answers to download | ||
| Verified skills on your profile and Skill Passport |
What Students Are Saying
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Testing ML Apps: common questions
1.Do I need experience before starting Testing ML Apps?
2.What can I try for free, and what needs payment?
3.How long does the course take?
4.What do I get when I finish?
Finish with proof, not just a certificate PDF.
Pass the final assessment and get a public verification page showing your score, completion date and the skills you demonstrated — ready to share on LinkedIn.
About this machine learning testing course
This machine learning testing course teaches testers how to check classic ML features, such as scoring, ranking and prediction models, before and after they reach users. It has 8 sections and 40 lessons, about 18 hours of study, with a Python lab and an assessment in every section and a final loan-model release review project.
You start with how ML features fail differently from normal code, then test training data quality, evaluation metrics and quality gates. Next you write behaviour, fairness and metamorphic tests, test prediction APIs and training-serving skew, check ML pipelines and safe model releases, and finally monitor data drift in production. It covers classic ML only; testing chatbots and LLMs belongs to the separate AI Testing course.
Who this course is for
- Manual or automation testers joining a team that ships ML features
- SDETs who want to add ML model testing to their skills
- QA engineers asked to sign off on model releases
- Testers preparing for data or MLOps-related QA interviews
What you need
- Some manual or automation testing experience
- Basic Python helps for the labs
- No maths beyond percentages
What you will be able to do
- Check training data for gaps, duplicates and leakage
- Choose evaluation metrics and set model quality gates
- Write behaviour, fairness and metamorphic tests
- Test prediction APIs and catch training-serving skew
- Review ML pipelines and plan safe model releases
- Monitor drift and decide when a model needs attention