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 run Watson NLP locally in Minikube. To set some context, check out the landing page IBM Watson NLP Library for Embed. The Watson NLP containers can be run on different container platforms, they provide REST and gRCP interfaces, they can be extended with custom models and they can easily be embedded in solutions.

The year 2020 will be remembered for a lot of things, among them a global pandemic, social justice and a historic election. These grabbed center stage because they have huge societal implications. Another big trend in 2020 has been the rise of artificial intelligence (AI). Historically, AI has been used by scientific institutions, big pharma, oil and gas and other data-heavy industries. Today, all businesses are data-heavy, and AI is being infused into everything and is being made available everywhere.

AI is no longer just a research project. It's solving real-world problems for organizations, which now need to figure out where to deploy their AI models to make faster decisions. With the convergence of AI, the Internet of Things and the approaching 5G infrastructure, the opportunity is ripe for companies to push their models beyond the data center to the edge, where billions of sensors are streaming data and making real-time decisions is a reality. Enterprises deploying AI workloads at scale are using a combination of on-premises data centers and the cloud, bringing the AI models to where the data is being collected. Deploying these workloads at the edge, say in a retail store or parking garage, can be very challenging if IT expertise is not available as one might have with data centers.