It's All About the Data - Continuously Improve ML Models the Data-Centric Way
Explore the fundamentals of data-centric machine learning practices in this comprehensive conference talk from MLOps World: Machine Learning in Production. Delve into techniques for detecting and debugging input data and label data quality issues, enhancing ML model performance through dataset and data slice analysis, and identifying post-deployment data problems to trigger improvement pipelines. Learn from Bernease Herman, Senior Data Scientist at WhyLabs and research scientist at the University of Washington eScience Institute, as she demonstrates how to debug an example ML model and system using the open-source whylogs library. Gain valuable insights into the growing trend of data-centric AI and understand why addressing dataset issues is crucial for preventing AI system failures and improving overall performance.