252: A Good Heuristic for Pain Tolerance
Katie Malone, a data scientist at Civis Analytics and host of the Linear Digressions podcast, discusses the practical application of machine learning in embedded systems. She explains the distinction between training and inference, highlighting the challenge of integrating black-box neural network outputs into deterministic embedded software. Malone emphasizes that data science is largely about data preparation and problem formulation, not just algorithm selection, and advises engineers to build projects to overcome imposter syndrome.