Next, students will explore the automation of machine learning workflows, with a focus on continuous integration and continuous delivery (CI/CD) pipelines, automated testing, and pipeline orchestration. This course also covers model deployment strategies, including containerization, serving models through REST APIs, and batch versus real-time inference, as well as important issues such as performance monitoring, data drift, model drift, and retraining strategies. In the final sessions, students are introduced to the evaluation and selection of MLOps tools and cloud platforms, along with the governance, security, and ethical considerations of operating AI systems in production.
