Durable Data Discovery: Making Exploratory Analysis Stick

Name: Durable Data Discovery: Making Exploratory Analysis Stick
Uploaded: 2021-11-17

Posted Nov 17, 2021 | Views 380

# Monitoring

# Presentation

# Coding Workshop

# Superconductive

SPEAKERS

James Campbell

Chief Technology Officer @ Superconductive

James Campbell is the CTO at Superconductive, the company behind the open-source data quality project Great Expectations, which he co-founded in 2017. Prior to that, he spent nearly 15 years working across a variety of quantitative and qualitative analytic roles in the US intelligence community.

James studied Mathematics and Philosophy at Yale and is passionate about creating tools that help communicate uncertainty and build intuition about complex systems.

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James studied Mathematics and Philosophy at Yale and is passionate about creating tools that help communicate uncertainty and build intuition about complex systems.

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Demetrios Brinkmann

Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

Building an effective ML pipeline requires understanding the data available to you and how it's changing. Exploring a new dataset is often an iterative, interactive process that gives the engineer doing it tremendous insight into the underlying data generating processes and the pipelines that have touched it. Yet too often, those insights are lost when a system goes into production or after internal handoff between teams. We talk about how to capture Exploratory Data Analysis done when first working with a dataset. With a clear understanding of what data characteristics were important in crafting a dataset, it becomes possible to collaborate on and share clear expectations about the true differentiator in ML pipelines -- the data that fuels them.

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