Beginning with Fusion 5.0, data scientists and machine learning engineers can deploy end-user-trained Python machine learning models to Fusion using the Data Science Toolkit Integration (DSTI). This offers real-time prediction and seamless integration with query and index pipelines.
Benefits:
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Extension points for data scientists to plug in customized Python modeling code
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Client libraries to ease the development and testing of Python plugins
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API-driven and dynamic, runtime loading and updating of plugins
Example use cases:
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Using SpaCy to extract named entities and indexing results into a Solr collection
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Using a Keras model to perform query intent classification at query time
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Using pre-trained word embeddings to generate synonyms for a query
See these topics for complete details: