I want to share my experience in the cloud, AI, and agents development areas.
In this blog post, we’ll take a look at Jekyll to get a basic understanding how usage and setup works. We won’t use an existing template, we’ll follow the initial steps for implementation and then we configure the source code for a custom deployment to GitHub pages.
This short blog post is about where you can find great simple tutorials for "Watson Libraries for Embed".
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.
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.
This blog post is about to deploy the IBM Watson Natural Language Processing Library for Embed to an IBM Cloud Kubernetes cluster in a Virtual Private Cloud (VPC) environment and is related to my blog post Run Watson NLP for Embed on IBM Cloud Code Engine. IBM Cloud Kubernetes cluster is a “certified, managed Kubernetes solution, built for creating a cluster of compute hosts to deploy and manage containerized apps on IBM Cloud“.
This blog post is about using the domain mapping functionality in IBM Cloud Code Engine to use custom domains and TLS certificates for your applications. The source code related to an example setup is in the GitHub project example WebApp build on Vue.js. But now we are going to use a wild card certificate for our domain *.example.com. With a TLS certificate for that domain.
This blog post is about using the IBM Watson Natural Language Processing Library for Embed on IBM Cloud Code Engine and is related to my blog post Run Watson NLP for Embed on your local computer with Docker. IBM Cloud Code Engine is a fully managed, serverless platform where you can run container images or batch jobs.
This blog post is about using the IBM Watson Natural Language Processing Library for Embed on your local computer with Docker. The IBM Watson Libraries for Embed are made for IBM Business Partners. Partners can get additional details about embeddable AI on the IBM Partner World page. If you are an IBM Business Partner you can get a free access to the IBM Watson Natural Language Processing Library for Embed. To get started with the libraries you can use the link Watson Natural Language Processing Library for Embed home. It is an awesome documentation and it is public available.
This is a short example/cheat sheet about, how to use the new module called terraform-ibm-observability-instances to plan service instances on IBM Cloud with Terraform. You can find the source code for the example of the blog post in this GitHub repository Example to use the IBM Observability Module. In this example we plan to create an Activity Tracker, a Log Analysis, and a Monitoring service instance on IBM Cloud with Terraform.
In this blog post I want to show, how to create a new realm with Keycloak REST API 20.0.1. The Keycloak API has changed and my older blog post How to create a new realm with the Keycloak REST API? doesn’t work anymore for version 20.0.1. I automate the Keycloak realm creation for an example realm by using cURL in a bash script. First I created the blog post about Export a Keycloak (Version 20) realm and now I show the creation of an example realm in Keycloak. I took a look in the new Keycloak REST API documentation and into the Keycloak Node.js client. In this blog post I use an example realm I exported before, here is the link to the example-realm.
Thomas Suedbroecker's Blog 