The podcast episode explores the critical importance of data quality in the context of AI and machine learning. It delves into the nuances of data quality for training and inference, the impact of contextual quality, and the need for continuous quality observation and observability in AI systems.

Takeaways

• Data quality for machine learning follows different rules than traditional data quality frameworks.
• Quality in AI systems is inseparable from observability and is more precise, contextual, dynamic, and architectural.

🎧 Listen to The Data Journey wherever you get your podcasts, or visit thedatajourney.com