I want to share my experience in the cloud, AI, and agents development areas.
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.
This blog post is about a simple example to use the Watson Assistant API v2 with the Node.js SDK to get a Watson Assistant sessionID and send a message to Watson Assistant using this sessionID. Here is the GitHub project watson-assistant-simple-node-js-server-example.
This blog post is about how export an example development realm using the Keycloak in version 20. I wanted to ensure that the export contains all information including users. You can find the relevant information in the Keycloak documentation ‘Exporting a realm to a file.’. I did some demo configurations in the new version and I can’t reuse my older exported examples from Keycloak.
My last blog post was about Watson Speech to Text language model customization and this blog post is about IBM Cloud Watson Text to Speech (TTS) custom voice model configuration. Because, now it's time to have some fun with the Watson TTS service. I created a fun customisation of the service that the German pronunciation sounds a little bit like the palatinate dialect. Here are the differences with two wav file I created with a custom Watson to Text to Speech voice model.
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.
IBM Watson Assistant is a SaaS offering from IBM to build conversational assistants. IBM Watson Assistant is using artificial intelligence which helps to understand users in context, to provide them easy and fast, consistent, and accurate answers across various applications, devices, or channels. IBM Watson Assistant is built on natural language understanding (NLU), natural language processing (NLP) and machine learning (ML). The first version was already very good, and IBM clients and partners were starting to take these advantages; for example Watson Assistant was used at the International Space Station. Here you can find some more details: CIMON brings AI to the International Space Station. Based on the feedback from clients, the IBM development and design team has created a brand new experience and added new functionalities to the service for example they expanded the integration possibilities with extensions. In this blog post I focus especially on custom extensions development and setup.
Infrastructure as Code and GitOps are ongoing big topics related to DevOps and CI/CD which needs effective automation to shorter the Software Development Lifecycle and simplify production deployments. In this blog post we don't talk much about these processes and methodologies. The blog post is more about how to reduce efforts to build an automation by using the IBM Accelerator Toolkit.
Thomas Suedbroecker's Blog 