Duration: 30 mins. 10 mins of slides, 15 mins live demo, and 5 mins Q&A.
Content: Brief introduction on how Decision Trees (DT) and Random Forest (RF) classifiers can be used in an IoT setting to solve ranking, regression, and classification problems locally at the device level. Then we demo how to efficiently port and execute DT and RF classifier models on MCU boards. The following tools are covered:
- micromlgen.
- sklearn-porter.
- m2cgen.
- emlearn.
Outcome: The audience can use the explained generic end-to-end method to quickly port and execute various datasets trained ML algorithms like DTs, RFs, SVMs, LGBM, XGB, AdaGrad, LogisticRegressionCV, etc. on any of the resource-constrained MCU-based devices of their choice/availability.