How to define a custom Open API specification for a Watson Machine Learning deployment to integrate it into watsonx Assistant

This blog post is about how to define a custom Open API specification` for Watson Machine Learning - IBM Cloud deployment to integrate it into watsonx Assistant. The Watson Machine Learning deployments make it easy for data scientists to write AI Prototypes to be integrated into applications because they can use Jupyter Notebooks and Python they are used to without knowing how to write containers and set up runtimes; they can deploy, and the developers can consume the AI functionalities they have implemented via a REST API.

How to create a model container image for Watson NLP for Embed

This longer blog post shows how to : … build a model init container with a custom model for Watson NLP for Embed. … upload the model init container to the IBM Cloud container registry. … deploy the model init container and the Watson NLP runtime to an IBM Cloud Kubernetes Cluster. … test Watson NLP runtime with the loaded model using the REST API.

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