Scaling your Data Science Workflows by Changing a Single Line of Code


Over the past decade, the democratization of data science tooling, particularly through Python libraries like pandas and NumPy, has empowered practitioners of all levels to work with data efficiently. Yet, despite the popularity of these tools, they present challenges as practitioners look to scale their workflows to production. In this talk, we explore the limitations of these tools and pain points that data scientists encounter when working with data at scale. I will share how our open-source project Modin (10M+ downloads) addresses this issue by seamlessly scaling up your pandas code with just a single line of code change.


Doris Lee is the CEO and co-founder of Ponder ( Doris received her Ph.D. from the UC Berkeley RISE Lab and School of Information in 2021, where she developed tools that help data scientists explore and understand their data. She is the recipient of Forbes 30 under 30 for Enterprise Technology in 2023.

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