When the Model Isn’t the Problem: How Data Gaps Undermine AI Systems
AI quality issues are on the rise and data + AI leaders are just beginning to feel the pain. One of the most common perpetrators? Data quality issues. At Monte Carlo, we’re no strangers to the impact of data quality—particularly at the scale and complexity of AI applications. However, we recently experienced that impact first-hand—and […]