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.
CheatSheet: How to loop an endpoint of an application running on “IBM Cloud Code Engine” with a bash automation
This blog post is a CheatSheet about how to loop an endpoint of an application running on IBM Cloud Code Engine with a bash automation.
Observe a running pod on IBM Cloud Code Engine with kubectl commands
In IBM Cloud Code Engine you also can use kubectl commands to get information about your running application in addition to the IBM Cloud Code Engine CLI.
Build and push a container image to IBM Cloud Container Registry using bash automation
This extract is from a bash automation script in the question-answering GitHub project. The bash script automates the deployment to IBM Cloud Code Engine. The extraction is about the building and pushing a container to the IBM Cloud Container Registry.
Show the collection IDs of IBM Cloud Watson Discovery projects using cURL
This blog post is a simple example (cheat sheet) of listing the collections for a project in Watson Discovery using cURL and the IBM Cloud Watson Discovery API V2. You can get more details in the IBM Cloud Watson Discovery API documentation. 1. Log on to IBM Cloud ibmcloud login (-sso) REGION=us-south GROUP=default ibmcloud target -r... Continue Reading →
The Setup of Bring Your Own Search (BYOS) for a Question Answering Service in Watson Assistant
This blog post is about how to set up Bring Your Own Search (BYOS) for the Question Answering Service based on IBM Software in Watson Assistant. The implementation of the Question and answering Service is available on GitHub.
Create a custom dictionary model for Watson NLP
This blog post is about, how to create a custom dictionary model for Watson NLP. One capability of the Watson NLP is the "Entity extraction to find mentions of entities (like person, organization, or date)." We will adapt the Watson NLP model to extract entities from a given text to find single entities like names and locations which are identified by an entry and its label.
Run Watson NLP for Embed in a KServe ModelMesh Serving environment on an IBM Cloud Kubernetes cluster in a VPC environment
This blog post is about to run Watson NLP for Embed example in a KServe ModelMesh Serving environment on an IBM Cloud Kubernetes cluster in a Virtual Private Cloud environment and reuses parts of the IBM Watson Libraries for Embed documentation.
Watson Speech to Text language model customization
This blog post is about IBM Cloud Watson Speech to Text (STT) language model customization. Currently I took a look at the IBM Cloud Watson Assistant service used to build conversational assistants. A conversation leads potentially to speech input of users, which needs to be converted to text to be processed using AI for example the NLU.
Use IasCable to create a Virtual Private Cloud and a Red Hat OpenShift cluster on IBM Cloud
In that blog post we use the IasCable framework to create a Virtual Private Cloud and a Red Hat OpenShift cluster on IBM Cloud. I covered the starting point for the IasCable framework in my last blog post “Get started with an installable component infrastructure by selecting components from a catalog of available modules with IasCable“