Data validation for machine learning Breck et al., SysML'19 Last time out we looked at continuous integration testing of machine learning models, but arguably even more important than the model is the data. Garbage in, garbage out. In this paper we focus on the problem of validation the input data fed to ML pipelines. The … Continue reading Data validation for machine learning

Continuous integration of machine learning models with ease.ml/ci: towards a rigorous yet practical treatment Renggli et al., SysML'19 Developing machine learning models is no different from developing traditional software, in the sense that it is also a full life cycle involving design, implementation, tuning, testing, and deployment. As machine learning models are used in more … Continue reading Continuous integration of machine learning models with ease.ml/ci