EMEA 2021 https://www.tinyml.org/event/emea-2021
Keynote: A novel approach to building exceptionally tiny, predictive and explainable models for non-data scientists
Blair NEWMAN, CTO, Neuton.ai
Performing compute and inference on the edge solves most issues with privacy, latency and reliability, but how do we address the remaining obstacles:
many parties interested in AI/ML, including those who work with microcontrollers, do not have knowledge in Machine Learning and software development
the difficulty of embedding large ML models into small compute devices
the challenge of evaluating the quality of a model, and whether it has interpretable, explainable and reliable output
Inference on edge devices will move toward mass adoption only if Machine Learning becomes available to non-Data Scientists. We will show how already, today, non-ML users can build – with just a few clicks and no-code – compact models which are up to 1000 times smaller than those built with Tensor Flow and similar frameworks (and without reduction of accuracy). We will demonstrate why models built with those frameworks are not optimal in size and accuracy and share how to overcome those obstacles and build quality compact models with an excellent generalization capability.
We will explain and show examples of how Neuton’s working tiny models can be embedded into microcontrollers and will compare the results with those built with TensorFlow Lite. We will also demonstrate how users can evaluate model quality at every stage and identify the logic behind the model analysis, therefore clarifying why certain predictions have been made.