Setting up a model for deployment in TensorFlow Serving involves configuring and packaging it in the TensorFlow SavedModel format. Properly defined input and output signatures ensure accurate request interpretation. Effective model version management is essential, allowing seamless updates. Implementing a health check endpoint helps monitor server readiness for predictions.