Deploying Natural Language Processing models in Watson Machine Learning

You can deploy a Natural Language Processing model by using Python functions or Python scripts. Both online and batch deployments are supported.

• The prediction output returned from <model>.run() is an object of a class specific to the concerned data model's prediction class (for example, watson_nlp.data_model.syntax.SyntaxPrediction). Such objects cannot be serialized into JSON format so the prediction output must be converted to either the Python dictionary type or JSON by using the <prediction output>.to_dict() (recommended) or <prediction output>.to_json() methods. If you don't convert the output, scoring API requests will return an error.
• If the Watson NLP add-on is installed, you can access the location of pre-trained models in the Python function code by using the LOAD_PATH environment variable.
• Prediction input payload and prediction response returned from score() must meet the requirements listed in online scoring and jobs API documentation.
• Scoring requests for NLP models may fail with an Out of Memory error reported by the underlying JVM runtime. If this happens, patch the deployment to use a hardware specification with more available memory.