How to deploy NLP: Named entity recognition (NER) example

In a detailed exploration of deploying Named Entity Recognition (NER) using Elasticsearch, the text guides readers through the process of utilizing an NER model to extract entities, such as people and locations, from unstructured text. The example uses a model from Hugging Face, deployed through Eland, to analyze the characters and settings in Les Misérables. By running the model via Docker, users can identify entities in text fields using the _infer API and integrate this into an ingest pipeline with Elasticsearch, enabling bulk inference. The process includes mapping text fields, configuring a pipeline in Kibana, and using scripting to categorize entities. Readers are shown how to visualize the data with tag clouds, and how to optimize performance by adjusting thread settings for better throughput and latency. The text also highlights the broader applicability of NLP in Elasticsearch, with additional tasks like text classification and sentiment analysis, encouraging users to explore further with Elastic Stack's new NLP features.