Production ML for Mission-Critical Applications - Robert Crowe
Video recorded at DevFest for Ukraine 2022 - a charitable tech conference that will bring together 20 industry-leading speakers over two days, featuring live streams from London and Lviv. It will address key topics for the future of tech, including trends in Android, Web, and AI.
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Deploying advanced Machine Learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. This is especially true for maintaining and improving model performance over the lifetime of a production application.
Unfortunately, the issues involved and approaches available are often poorly understood.
We discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TFX, as well as available tooling for rigorous analysis of model performance and sensitivity. Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology including testability, hot versioning, and deep performance analysis.
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