Machine learning's growing adoption in enterprises highlights the importance of machine learning operations (MLOps) for efficiently scaling production capacities and delivering significant business results. Diego M. Oppenheimer, CEO of Algorithmia, discusses with Dana Lawson the challenges organizations face, such as security, governance, and toolchain integration, and the need for a canonical stack to streamline AI and ML processes. Despite increased budgets and staffing, many organizations struggle with technical debt, operational inefficiencies, and compliance with regulations. The AI Infrastructure Alliance aims to establish open standards for a portable, scalable ML ecosystem, enabling seamless deployment across various environments. Automation, especially in deployment, enhances model governance and operational efficiency, as exemplified by Algorithmia's integration with GitHub Actions for secure and cost-effective model productionization.