This session introduces Machine Learning Operations (MLOps), focusing on the challenges of operationalizing AI and the differences between DevOps and MLOps practices. The presenter, John, discusses the importance of collaboration between data scientists, ML engineers, and DevOps teams to successfully implement MLOps. He covers key aspects such as data preparation, model training, deployment, and monitoring, emphasizing the need for automated workflows and governance. The talk also explores the four stages of MLOps maturity, from initial experimentation to scalable, production-ready systems. Throughout the presentation, John highlights how AWS services like SageMaker can facilitate MLOps processes and help organizations mature their ML practices.