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Eliminating Garbage In/Garbage Out for Analytics and ML

MLOps.community via YouTube

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Overview

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This podcast discusses how to shift data quality ownership upstream and make data observability easier for users of analytics and machine learning workflows. It also considers stakeholder needs and the user experience of data workflow tools.

Syllabus

[] Santona's and Roy's preferred coffee
[] Santona's and Roy's background
[] Takeaways
[] Please like, share, and subscribe to our MLOps channels!
[] Back story of having Santona and Roy on the podcast
[] Santona's story
[] Optimal tag teamwork
[] Dealing with stakeholder needs
[] Having mechanisms in place
[] Building for data Engineers vs building for data scientists
[] Creating solutions for users
[] User experience holistic point of view
[] Tooling sprawl is real
[] LLMs reliability
[] Things would have loved to learn five years ago
[] Wrap up

Taught by

MLOps.community

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