Computer vision models often fail in production due to various reasons. Poor data labeling errors can lead to incorrect patterns being learned by the model, while poor data quality issues such as duplicates, noise, and unrepresentative data can compromise the model's accuracy. Data drift occurs when the statistical properties of the real-world images a model encounters in production change over time, diverging from the samples it was trained on. Additionally, neglecting post-deployment maintenance and treating deployment as the final step can lead to model staleness and eventual failure. Understanding these failures is crucial to learning best practices for solving or avoiding them, which include adopting tools like Encord Active, monitoring data drift, and implementing machine learning operations (MLOps) practices such as model monitoring, logging, and governance.