Machine learning teams have produced some excellent tools in the last decade to assist data scientists and machine learning engineers in their day to day work. However, teams are often left to their own devices when putting all the blocks together to build workflows. This introduces a lot of complexity, leaves workflows prone to error, and exacerbates tensions between teams due to fundamental differences in how they operate. lolpop is a new open source software engineering framework Jordan built for machine learning workflows. The overarching goal is to provide a framework that can help unify data science and machine learning engineering teams. We believe by establishing a standard framework for machine learning work that teams can collaborate more cleanly and be more productive.
In this talk, we'll introduce the tool, the motivations behind it, and demonstrate how it can begin to help teams streamline their ML use across all their use cases.
Jordan Volz is the creator of lolpop, an open source project that provides a software engineering framework for machine learning workflows. Jordan has worked in big data and machine learning over the past two decades, and he has helped companies of all shapes and sizes adopt ML use cases. He has held positions at leading technologies companies such as: Cloudera, Voltron Data, Continual, Dataiku, Autonomy, and Epic. He currently is the Head of Field Engineering at Voltron Data, where he's helping clients build and adopt composable data systems. Jordan is passionate about bringing software engineering best practices to ML teams, both big and small.