The Implementation Cycle in Applied Natural Language Processing (NLP)

The implementation cycle in applied natural language processing (NLP) is a cyclical process that involves prototyping, machine learning operations (MLOps), and continuous testing and evaluation to ensure the system remains up-to-date and solves the actual problem it's intended to solve. Unlike traditional NLP, applied NLP focuses on leveraging pre-trained models and sharing resources to make NLP more accessible to developers. The process begins with designing a system pipeline, choosing suitable language models, building a prototype, experimenting, evaluating, and testing, followed by fine-tuning the models with real-world data. MLOps takes over after deployment, monitoring the system's performance, collecting feedback from real users, and ensuring the system remains up-to-date. The cycle is repeated periodically to ensure continuous improvement.

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