Building Resilient ML Models that are Robust to Adversarial Attacks is a critical aspect of modern machine learning research and development. Adversarial attacks pose a significant threat to the integrity and performance of machine learning models, making it essential for practitioners to understand the vulnerabilities in their systems and adopt strategies to enhance resilience. In this article, we delve into the world of adversarial attacks in machine learning, explore the techniques for building robust models, and discuss the importance of adversarial training and optimization in defending against malicious attacks.