Deep Learning at Scale

The advent of modern deep learning techniques has given organizations new tools to understand, query, and structure their data. However, maintaining complex pipelines, versioning models, and tracking accuracy regressions over time remain ongoing struggles of even the most advanced data engineering teams. This talk presents a simple architecture for deploying machine learning at scale and offer suggestions for how companies can get their feet wet with open source technologies they already deploy.