SensiML’s AutoML process uses the power of cloud computing to aid in the model creation process for ML classifiers intended to run on the most resource-constrained computing platforms at the IoT edge. Ordinarily, creating ML models for this class of devices requires a unique blend of skillsets that include domain knowledge for the application use case, data science and numerical methods expertise in signal transformation and classification algorithms, and firmware programming and optimization expertise to condense theoretical ML pipelines to practical and efficient code that fits the device.
Regardless of whether your development team possesses one or all of these unique skills, the effort and cost of developing such algorithms by hand or with general-purpose AI frameworks is not scalable and often adds unnecessary cost and risk to fixed product schedules. Alternatively, the use of AutoML can greatly accelerate the model development and optimization process by augmenting or replacing manual programming with automation. Search optimization algorithms can traverse many thousands of model permutations delivering results in minutes to hours of machine time. The search space can also be much expanded as SensiML’s AutoML tool considers not just a single classification technique such as neural networks, but rather a library of classifiers that range from classic decision trees to distance-based classifiers to neural networks. The best model is the one the provides the most accurate results with as little compute latency, power consumption, and memory use as possible.
