Speaker "Lawrence Chernin" Details Back



Validation Methodology of Large Unstructured Unsupervised Learning Systems


Snapwiz's Data Science team uses multiple levels of validation that cover various models in the domains of adaptive learning, skill networks, assessments, professional development and career recommendations. The models that power our applications includes psychometric, machine learning tools, natural language processing and custom built. I will describe our best practices and show validation of specific examples in our applications. This includes a suite of specific unit tests that cover specific logical implementations of algorithmic equations to automated cron jobs that daily run thousands of simulated user cases through the end-to-end application. Once failures are detected, the QA team has a suite of tools that they can use to do first level debugging before passing off to developers.


Lawrence has work in the data science field for six years. Prior to data science he worked in the semiconductor software field for many years and originally worked as an astrophysicist at Berkeley. He has a PhD from Harvard University and is an Kaggle enthusiast.