Model packaging refers to the process of organizing and preparing machine learning models for deployment and use in real-world applications. This involves creating a structured format that includes not only the model itself but also essential metadata, such as versioning, dependencies, and performance metrics. Effective model packaging enhances reproducibility, facilitates collaboration among data scientists, and ensures that models can be easily integrated into various systems. By standardizing how models are packaged, organizations can streamline their workflows, improve model management, and promote transparency in AI systems, ultimately leading to more reliable and efficient applications.