FastAPI is a new Python API framework that is more and more used in production today. We are using FastAPI under the hood behind NLP Cloud. NLP Cloud is an API based on spaCy and HuggingFace transformers in order to propose Named Entity Recognition (NER), sentiment analysis, text classification, summarization, and much more. FastAPI helped us quickly build a fast and robust machine learning API serving NLP models. Let me tell you why we made such a choice, and show you how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER).

While visual'no code' tools are helping businesses get more out of computing without the need for armies of in-house techies to configure software on behalf of other staff, access to the most powerful tech tools -- at the'deep tech' AI coal face -- still requires some expert help (and/or costly in-house expertise). This is where bootstrapping French startup, NLPCloud.io, is plying a trade in MLOps/AIOps -- or'compute platform as a service' (being as it runs the queries on its own servers) -- with a focus on natural language processing (NLP), as its name suggests. Developments in artificial intelligence have, in recent years, led to impressive advances in the field of NLP -- a technology that can help businesses scale their capacity to intelligently grapple with all sorts of communications by automating tasks like Named Entity Recognition, sentiment-analysis, text classification, summarization, question answering, and Part-Of-Speech tagging, freeing up (human) staff to focus on more complex/nuanced work. OpenAI built a text generator so good, it's considered too dangerous to release Production ready (pre-trained) NLP models for English are readily available'out of the box'. There are also dedicated open source frameworks offering help with training models.

High performance production-ready NLP API based on spaCy and HuggingFace transformers, for NER, sentiment-analysis, text classification, summarization, question answering, and POS tagging. Deploy your own spaCy models. No DevOps required.