Knowledge distillation is a model compression approach that transfers learned knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model. This process enables organizations to deploy lightweight models that retain much of the original model's predictive power while requiring significantly fewer computational resources. Knowledge distillation mirrors human educational processes where experienced instructors guide novice learners through complex concepts in AI systems. A pre-trained teacher model with superior performance serves as the knowledge source, while a smaller student model learns to replicate the teacher's decision-making patterns. This relationship enables efficient knowledge transfer without requiring the student to learn from scratch. Teacher models provide multiple forms of guidance beyond simple output predictions, including attention patterns, intermediate layer representations, and probability distributions across all possible classes. The student model learns to match these various aspects of the teacher's behavior, developing similar internal representations despite its reduced architectural complexity. This comprehensive learning approach ensures that the compressed model captures the essential reasoning patterns that drive the teacher's performance, contributing to AI model explainability. Knowledge distillation finds applications across numerous domains where computational efficiency directly impacts operational success and business outcomes, including mobile and edge computing, autonomous systems, cloud cost optimization, IoT and industrial applications, and enterprise SaaS platforms. The technique offers unique advantages depending on the specific requirements of the deployment scenario, model architecture, and performance objectives. Four key techniques for knowledge distillation include response-based distillation, feature-based distillation, progressive knowledge distillation, and online knowledge distillation. Each approach has its strengths and weaknesses, and the choice of technique depends on the specific needs of the project. To evaluate knowledge distillation effectiveness, comprehensive assessments are required that extend beyond traditional accuracy metrics to capture the nuanced performance characteristics of compressed models. This involves implementing comprehensive evaluation metrics, using production environment validation strategies, and establishing performance drift detection systems. Galileo is a modern platform that streamlines the entire distillation workflow for enterprise deployment, providing automated model comparison and analysis, real-time production performance monitoring, comprehensive evaluation metrics, intelligent error analysis and debugging, and seamless integration with ML operations pipelines. By deploying knowledge distillation with confidence using Galileo, organizations can ensure that their compressed models deliver both performance and operational efficiency required for successful AI initiatives.