A natural language processing model can be an extremely effective tool for extracting information from unstructured text data.
NLP models are a statistical tool for predicting words by analyzing patterns of language.
As a result of NLP, a model can combine many capabilities, including audio-to-text conversion, speech recognition, sentiment analysis, summary, and spelling checking.
When an NLP model is in production, a number of issues can arise, including: data drift, missing value rates, unexpected new values, etc. To make sure your model is working as intended, it’s important to monitor your NLP models in production, detect issues early, and intervene when necessary.