I want to share my experience in the cloud, AI, and agents development areas.
Everyone is now talking about this new way of using AI in an interactive form of communication. When we talk about free or open and AI, these three questions immediately came to my mind: “If you're not paying for the product, then you are the product.” That's a quote from Daniel Hövermann in The Social Dilemma. What will be the business model? "Will my Job be replaced?" "Can I trust, and what is my remaining responsibility?"
This is a short cheat sheet on the available parameters for regular expressions.
Cheat Sheet: Most of the Mac users know the challenge: "Finding a special character on a Mac keyboard isn't easy on a Mac sometimes."
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.
This is a short blog post about assigning read/write permissions for all users to the unpacked "custom model files and folders".
This blog post is about, how to customize a classification model for Watson NLP for Embed and use it on your local machine.
This blog post is about to deploy the IBM Watson Speech to Text Library for Embed to an IBM Cloud Kubernetes cluster. 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“.
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.
Thomas Suedbroecker's Blog 