Model serving techniques refer to the methods and frameworks used to deploy machine learning models into production environments, enabling them to make predictions on new data. These techniques are crucial for integrating models into applications, ensuring they can handle real-time requests efficiently. Various architectures exist, including synchronous and asynchronous serving, batch processing, and on-demand predictions. Tools like TensorFlow Serving, MLflow, and Databricks provide robust solutions for managing the lifecycle of machine learning models, from deployment to monitoring. Understanding these techniques is essential for data scientists and engineers aiming to operationalize their models effectively.