Avoid the Top 5 Web Data Pitfalls When Developing AI Models

Learn how to avoid critical web data pitfalls when developing AI models in this 40-minute sponsor presentation by Jakub Glodek from Bright Data at PyCon US. Explore five key challenges in AI model development, including data bias, insufficient data variety, overfitting and underfitting, poor data quality, and data drift. Discover strategies to ensure fair and representative training data, incorporate diverse scenarios, balance model complexity, maintain high data quality, and adapt to evolving real-world conditions. Access the presentation slides to gain valuable insights on creating reliable and accurate AI models that can effectively generalize to new data and maintain performance over time.