Motokolearn is a Motoko package meant to facilitate on-chain training and inference of machine learning models where having a GPU is not a requirement.
Project github: GitHub - ildefons/motokolearn
- Web3 services using on-chain trained models can inherit security and verification capabilities from the underlying Internet Computer protocol
- Overall dapp architecture can be simplified by eeliminate dependencies with external web2 providers and/or avoid using pre-compiled WASM modules of pre-trained machine learning models
- Small to medium size data problems of heterogenous “tabular” data is often better solved with ensemble of boosted trees
- From personal experience, 1) many Kaggle challenges are better solved with ensembles of trees; and 2) last year alone, I consulted with three medium sized startups and all projects involved data bases below 100 megabytes and none of them required the use of large neural network nor GPUs.
Installation and tutorial: motokolearn/README.md at master · ildefons/motokolearn · GitHub
Final Dfinity Grant milestone review
2) Do mops package
3) Integration of linear algebra library (linear solver and matrix decomposition functionalities)
4) Development of filtering methods for efficient sequential model updates (Kalman filter, recursive least squares)
5) Development of clusting methods (K-means, spectral clustering)
6) Community support