Run Watson NLP for Embed on IBM Cloud Code Engine

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.

Open the door wide open for Watson Assistant with “custom extensions” – an awesome progression

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.

New Open-Source Multi-Cloud Asset to build SaaS

When software is provided as a managed service (SaaS), using a multi-tenant approach helps minimise costs for the deployments and operations of each tenant. In order to leverage these advantages, applications need to be designed so that they can be deployed to support multiple tenants, while maintaining isolation for security reasons. At the same time, common deployment and operation models are required so that new SaaS versions can be deployed to existing tenants, or to onboard new tenants, in a reliable and efficient way.

How to use environment variables to make a containerized Quarkus application more flexible

When you run a containerized application on a container orchestration platform like Kubernetes, Open Shift or with a serverless framework like Knative or Code Engine or on other platforms, it is helpful to pass endpoints to other applications to the containerized application by using environment variables. When the container will be restarted, these variables can be provided to the container and no adjustment in the source code is necessary. You can use configmaps or in Code Engine simple the environment variable itself.

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