Trustworthy Data for Machine Learning

The most common challenge for ML teams operating at scale is data quality. In this talk, Chad discusses how Convoy invested in a large-scale data quality effort to treat data as an API and provide a data change management surface to enable trustworthy machine learning.

Take-aways

- Data quality is a top-down, not bottoms-up initiative - Engineers should own upstream data quality by treating data as code - Data teams should evolve the knowledge layer to drive event development

In this episode

Chad Sanderson

Head of Product, Data Platform, Convoy

Chad Sanderson is the Product Lead for Convoy's Data Platform team, which includes the data warehouse, streaming, BI & visualization, experimentation, machine learning, and data discovery. Chad has built everything from feature stores, experimentation platforms, metrics layers, streaming platforms, analytics tools, data discovery systems, and workflow development platforms. He’s implemented open source, SaaS products (early and late-stage) and has built cutting-edge technology from the ground up. Chad loves the data space, and if you're interested in chatting about it with him, don't hesitate to reach out.

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Chad Sanderson

Head of Product, Data Platform, Convoy

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Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.