Data Science and Machine Learning is increasingly a collaborative process. For teams to collaborate effectively they must be able to reproduce experiments, track data provenance and easily compare the efficacy of their models.
Our research with data scientists and machine learning practitioners from companies with machine learning at the core of their business enabled us to identify a set of challenges that are common across the industry. We also observed large differences in the devops and infrastructure support provided to data science within organizations. The larger, more engineering-focussed companies have responded to these challenges by investing huge sums to build custom versioning, monitoring and collaboration tooling, but this approach isn’t viable for everyone.
In this poster session we’ll show you some of our findings and take you through how the dotscience suite of tools and services can help you to scale your own data science and machine learning initiatives.
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