At QCon SF 2024, experts highlighted a significant challenge in the field of machine learning—about 85% of models developed in labs fail to reach production due to obstacles like real-world data integration, security reviews, and scaling issues. This results in wasted resources and diminished stakeholder confidence. MLOps emerges as a solution, integrating versioning, automated pipelines, monitoring, and governance to transition from experimental models to robust, scalable systems. Unlike traditional DevOps, which focuses on deterministic code, MLOps caters to the non-deterministic nature of machine learning by incorporating model-specific validations and monitoring for data drift and accuracy. The approach offers numerous benefits, including faster deployment, improved model reliability, scalable operations, enhanced compliance, reduced costs, and accelerated innovation. MLOps relies on pillars like model versioning, automated training and deployment pipelines, infrastructure orchestration, and data pipeline automation to ensure consistent delivery of business value. The text further outlines strategic steps to operationalize machine learning, emphasizing the need for formalized evaluation standards, automated CI/CD pipelines, comprehensive monitoring, drift detection, governance frameworks, scalable workflows, and continuous feedback loops. The platform Galileo is mentioned as a tool that accelerates the MLOps process with features like automated evaluation, real-time monitoring, integrated quality gates, regulatory compliance, and unified workflow management.