From Notebook to Market: Engineering Sub-Millisecond ML Trading Pipelines

Moving machine learning models from research environments to live trading systems requires solving architectural challenges that go far beyond model accuracy. In institutional trading, even milliseconds of pipeline latency can render predictions worthless and eliminate profitability, making backend engineering as critical as the algorithms themselves. This article examines the production infrastructure and design patterns that separate institutional-grade trading systems from academic prototypes.