Deploying AutoML models in a production environment presents challenges such as scalability, reliability, and integration with existing systems. AutoML tools address these challenges through strategies like containerization, which encapsulates the model and its dependencies, model serving APIs, which provide a scalable and efficient way to serve predictions, and CI/CD pipelines, which automate the deployment process and ensure continuous integration and delivery. These strategies facilitate seamless integration with existing systems and enable smooth deployment and management of AutoML models in production.