Eliminating Garbage In/Garbage Out for Analytics and ML

Overview

Dive into a thought-provoking podcast episode featuring Roy Hasson and Santona Tuli as they discuss strategies for eliminating "garbage in, garbage out" in analytics and machine learning. Learn about shifting left data quality ownership and implementing observability to catch bad data at the source, preventing it from entering your Analytics/ML stack. Explore topics such as optimal teamwork in data engineering, addressing stakeholder needs, building solutions for different user types, and the challenges of tooling sprawl. Gain insights into LLM reliability and valuable lessons learned in the field of data engineering and science. This 51-minute discussion offers practical takeaways for professionals working with data quality, ML pipelines, and analytics 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