The webinar titled "Life vs. ImageNet" hosted by Lakera explored the complexities and challenges of transitioning machine learning (ML) and computer vision technologies from academic settings to real-world applications. Panelists from various industries, including autonomous driving, healthcare, and technology, shared insights into the significant differences between academic ML, which often begins with defined datasets and focuses on model performance, and real-world ML, which prioritizes product specifications and user needs. Key takeaways emphasized the importance of adopting a product-first mentality, understanding diverse evaluation metrics, and addressing challenges unique to scaling ML systems, such as data representation, robustness, and transparency. The discussion also highlighted the challenges faced by traditional industries and the rise of foundation models, which present new complexities due to their pre-trained nature and the potential biases in the data they use. The panelists underscored the necessity of robust evaluation frameworks and a focus on testing and validation to build trust and transparency in AI solutions.