Best practices for deploying NLP models involve understanding user requirements, avoiding one-shot development cycles that can be brittle and expensive to retouch, and ensuring sufficient feedback, testing, and evaluation throughout the development process. Initial planning should include a clear vision from stakeholders, while rapid prototyping allows for gaining maximum usage data without investing too much effort in the initial model. The quality of the training set is crucial, with a focus on high-quality annotators to avoid long-term detriments to the development cycle. Pipelines and iteration are essential for enabling rapid cycling through options, and tools like Haystack can help organize production into modular parts that are easy to swap out while experimenting. By adopting an iterative approach and keeping steps bite-sized, developers can increase their project's chances of success and ensure a virtuous cycle of feedback and tooling.