Reproducibility in ML Development: Optimizing Collaboration

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Presented by

Kristen Kehrer, Developer Advocate at Comet, Chase Fortier, MLOps Engineer at Comet

About this talk

Research shows that one of the top challenges faced by data scientists and machine learning (ML) engineers is reproducibility, or the ability to run an algorithm on particular datasets with similar results. If you are part of a mid-market or enterprise team looking to advance your use of ML, reproducibility can be a barrier to ensuring positive outcomes and scaling great work. In this webinar, you will learn about: The four aspects of reproducibility in machine learning A five-point reproducibility checklist you can use to ensure reproducibility in ML experimentation across your organization How a machine learning platform can assist with ML reproducibility

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Comet provides a self-hosted and cloud-based MLOps solution that enables data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams. Learn more at www.comet.ml