CaseTalkMachine Learning Canvas

Structured thinking for ML-powered products · Louis Dorard (CC BY-SA 4.0)

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Value Proposition What business decision or action will be improved by this ML component?
Data Sources What data do you have access to? Internal, external, structured, unstructured?
💡 Well-defined data sources need semantic clarity. CaseTalk provides an FCO-IM model to define your data features precisely. Learn more →
ML Task Regression? Classification? Clustering? Recommendation? Which type of ML problem?
Prediction Target What are you predicting? Define the output/label precisely.
Features What input variables will you use? How will you engineer them?
Model & Evaluation Which model type? What metrics? Baseline performance?
Decisions & Actions How will predictions be used? Human in the loop? Automated?
Feedback Loop How will you know if the model performs well in production? How do you retrain?
Build / Make / Buy & Risks Build from scratch? Use existing ML platform? Buy a solution? Key risks?