Making them succeed.
WHAT KILLS PROJECTS
No clear decision the prediction supports Data that does not exist or is inadequate No route to production Nobody to maintain it Expectations set by demonstrations rather than evidence
WHAT TO DO AT THE START
Establish the decision, the data, and the deployment path.
WHAT TO BUILD FIRST
The thinnest end-to-end path, with a trivial model.
WHY
It proves the path exists, which is the real risk.
WHAT TO AVOID
Months of modelling before anyone has seen a prediction in place.
WHAT TO SET EXPECTATIONS ABOUT
That the model will be wrong sometimes What the error rate is likely to be What it will cost to run and maintain
WHAT TO AGREE
What performance is good enough to deploy.
WHY BEFORE BUILDING
Afterwards, the answer becomes whatever was achieved.
WHAT TO DEMONSTRATE
Real predictions on real data, early.
WHAT TO MEASURE AFTER DEPLOYMENT
The business outcome, not only model metrics.
WHAT TO BE PREPARED TO DO
Recommend not deploying, when the evidence does not support it.
WHAT TO DOCUMENT AT THE END
What worked, what did not, and what you would do differently.
