The integration of JFrog Artifactory with Amazon SageMaker is aimed at enhancing the development, deployment, and management of machine learning (ML) models by incorporating comprehensive artifact management into the ML lifecycle. Artifactory's role in this integration is to manage, version, and secure ML models and their dependencies, ensuring immutability and traceability within a DevSecOps framework. This collaboration allows developers to streamline workflows by facilitating the retrieval and storage of artifacts, which helps address potential issues such as dependency conflicts, security vulnerabilities, and licensing constraints. By leveraging Artifactory's robust infrastructure within SageMaker's fully managed service, organizations can benefit from improved efficiency, customization options, and regulatory compliance in AI/ML projects. The guide elaborates on the setup and practical application of integrating Artifactory with SageMaker, illustrating workflows for training and deploying ML models, and highlights the importance of securing and customizing the SageMaker environment to maximize the advantages of this integration.