Eliminating Garbage In/Garbage Out for Analytics and ML
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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.