Serving NLP Models with MLflow
Published on
by 
James Ravenscroft
MLFlow is a powerful open source MLOps platform with built in framework for serving your trained ML models as REST APIs. The REST framework will load data provided in a JSON or CSV format compatible with pandas and pass this directly into your model. This can be handy when your model is expecting a tabular list of numerical and categorical features. However it is less clear how to serve with models and pipelines that are expecting unstructured text data as their primary input. In this post we will explore how to train and then serve an NLP model using MLFlow, scikit-learn and spacy.
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