Fixing Data Quality at Scale with Data Observability

Discover how to address data quality issues at scale using data observability in this 54-minute workshop session from MLOps World: Machine Learning in Production. Learn from Barr Moses and Lior Gavish, CEO & Co-Founder and CTO & Co-Founder of Monte Carlo respectively, as they delve into the challenges of maintaining data reliability in production environments. Explore common problems such as funky product dashboards, drifting ML models, and broken datasets that plague data teams. Gain insights on how to move beyond reactive, ad hoc approaches to data quality management and implement proactive strategies using data observability techniques. This session is ideal for data professionals seeking to improve the reliability and effectiveness of their data pipelines and machine learning models in production.