The Rise of the Machine Engineer: Alex Zamoshchin from Lyft

Lyft relies on Machine Learning (ML) Engineers to bridge the gap between data scientists who develop models and teams that operationalize them. The company's ML infrastructure is used for various solutions, including mapping, fraud detection, pricing optimization, and ETA estimates. Alex Zamoshchin, an engineering manager at Lyft, explains how ML engineers help get models from research into the real world while ensuring they achieve business objectives. They are involved in framing ML problems within the business context, converting models into working pipelines, and analyzing experimental and observational data to ensure model quality and performance once deployed. In a hypothetical world without ML engineers, issues with models could arise before or after implementation, leading to potential failure in production environments.