This post is part of a mini series which describes an AI text classification sample end to end using Watson NLP (Natural Language Understanding) and Watson Studio. This part explains how to train custom models with Watson NLP and Watson Studio. (more…)

This post is part of a mini series which describes an AI text classification sample end to end using Watson NLP (Natural Language Understanding) and Watson Studio. This part explains how to label sentences via the open source tool Label Sleuth. (more…)

This post is part of a mini series which describes an AI text classification sample end to end using Watson NLP (Natural Language Understanding) and Watson Studio. This part explains how to convert XML RSS feeds into one CSV with a list of all sentences. (more…)

This post is the first one of a mini series which describes an AI text classification sample end to end. The sample covers the AI lifecycle, from data preparation, over training to deployment on Kubernetes. Watson NLP (Natural Language Understanding) and Watson Studio are used for the training. At runtime Watson NLP is run as container on Kubernetes. (more…)

IBM Watson NLP (Natural Language Understanding) and Watson Speech containers can be run locally, on-premises or Kubernetes and OpenShift clusters. Via REST and gRCP APIs AI can easily be embedded in applications. This post explains how to find the latest versions of containers and how to get the model files and gRPC proto files. (more…)

IBM provides Watson NLP (Natural Language Understand), Watson Speech To Text and Watson Text To Speech as containers which can be embedded in cloud-native applications. This post lists links to relevant information in the context of Embeddable AI from IBM and the three libraries. (more…)

IBM Watson NLP (Natural Language Understanding) and Watson Speech containers can be run locally, on-premises or Kubernetes and OpenShift clusters. Via REST and gRCP APIs AI can easily be embedded in applications. This post describes how to deploy and run Watson NLP on the serverless offering IBM Code Engine. (more…)

IBM Watson NLP (Natural Language Understanding) and Watson Speech containers can be run locally, on-premises or Kubernetes and OpenShift clusters. Via REST and gRCP APIs AI can easily be embedded in applications. This post describes how to deploy and run Watson NLP and Watson NLP models on Kubernetes via the highly scalable model inference platform KServe ModelMesh. (more…)

IBM Watson NLP (Natural Language Understanding) and Watson Speech containers can be run locally, on-premises or Kubernetes and OpenShift clusters. Via REST and gRCP APIs AI can easily be embedded in applications. This post describes different options how to run and deploy Watson NLP. (more…)

IBM Watson NLP (Natural Language Understanding) and Watson Speech containers can be run locally, on-premises or Kubernetes and OpenShift clusters. Via REST and gRCP APIs AI can easily be embedded in applications. This post describes how custom Watson NLP models can be deployed with TechZone Deployer, an opinionated deployment and operations toolkit based on Terraform and ArgoCD. (more…)