- Statistics, Mathematics and algorithms behind ML and AI
- ML workflows(technology agnostic) – data prep, model train, test, infer
- AI workflows(technology agnostic) – data prep, network training, test, infer
- Computer Vision workflows
- Feature engineering – unsupervised/supervised learning, feature extraction, visualization, PCA
- Tools(any one) – Python sci-kit, Tensorflow, Matlab
- Data visualization
- Run inference engines on constrained devices(like Raspberry Pi)
- Run models in cloud environments – Google or Azure.
- IoT domain knowledge
- Create and retrain models for new sets of data. Publish executive summary reports on new models created.
- Work closely with IoT Platform team to understand data patterns, understand business need.
- Integrate model inferencing with IoT Platform