All learning series · Suite application · Part 1
Maximo Predict
Interpret predictive outputs with their data and model context.
Before you start
MAS learning path. Match the component documentation to your installed release and maintenance build before configuring it.
- Predict and the documented analytics dependencies for your deployment.
- A training dataset with asset identity, measurements and trustworthy outcome labels.
IBM sources + Assetize exercise · Not lab-verified
01 · Basics — Understand the building blocks
Predict builds on asset-performance information to estimate degradation or failure. Its outputs need interpretation in the context of the model and the data used.
A prediction is not an observed failure. Validate performance on data that was not used to train the model.
Topics in this series
- Prediction target
- Data quality
- Training and evaluation
- Interpreting outputs
- Operational feedback
02 · Configuration — Work through the setup
Use a training environment and match the actions to the IBM guide for your installed build.
- Define the prediction target and time horizon before selecting a model.
- Inspect data completeness, timestamps and the meaning of failure labels.
- Separate training and evaluation periods so future outcomes cannot leak into training inputs.
- Follow the documented model workflow and review predictions for the held-out population.
- Compare false alarms, missed events and lead time before defining a maintenance response.
03 · Practical example
Fictional teaching example
A fictional pump prediction warns of elevated failure probability within 30 days. That horizon is different from predicting failure tomorrow and requires a different response policy.
04 · Practice and verify
Create an evaluation table with prediction time, horizon, prediction, observed outcome and chosen action.
Expected result
Each result has a model/data context and can be evaluated against later observations without using those observations as inputs.
Common mistakes
- Using future maintenance outcomes as training features.
- Presenting a probability as certainty.
05 · Advanced concepts
Explore drift, retraining and action thresholds. Track whether interventions change the observed outcome and therefore the interpretation of model accuracy.
IBM references
Product explanations link to IBM sources. Scenarios and exercises are original Assetize guidance, not claims of executed lab procedures. Reviewed 2026-10-01.
Maximo ecosystem overview · Manage learning paths · Practice data in Workbench