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.

Cloud native deployment with Kubernetes orchestration has enabled the "Write Once, Deploy Anywhere" paradigm for applications. This application development and deployment model enables scale and agility in today's hybrid and multi-cloud environments. Applications or services packaged as containers can be deployed and managed with the same Kubernetes based eco-system tools in the public cloud, on premise or Edge locations. Microsoft Azure Arc-Enabled Kubernetes (Reference 1) could be viewed as one such ecosystem tool the enables central management of Kubernetes clusters deployed on premises locations or across different public clouds. Kubernetes based offerings from different vendors are supported and they need not be based on Azure Kubernetes Service (AKS) (Reference 2).

This article was published as a part of the Data Science Blogathon. A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer. The Model development process undergoes multiple iterations and finally, a model which has acceptable performance metrics on test data is taken to the production environment. Taking the final chosen model reaching it out to the users is called deployment and there are a few options available to deploy a model. Kubernetes(also called k8s) is one of the open-source tools used for deploying our applications.

This repository contains the core services of the FfDL (Fabric for Deep Learning) platform. FfDL is an operating system "fabric" for Deep Learning Once installed, use the command make minikube to start Minikube and set up local network routes. The minimum recommended capacity for FfDL is 4GB Memory and 2 CPUs. If you already have a FfDL deployment up and running, you can jump to FfDL User Guide to use FfDL for training your deep learning models. If you are getting started and want to setup your own FfDL deployment, please follow the steps below.

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions. This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs. Our goal is to help folks use ML more easily, by letting Kubernetes to do what it's great at: Because ML practitioners use so many different types of tools, it is a key goal that you can customize the stack to whatever your requirements (within reason), and let the system take care of the "boring stuff." While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions. This document details the steps needed to run the kubeflow project in any environment in which Kubernetes runs. Our goal is to help folks use ML more easily, by letting Kubernetes to do what it's great at: Because ML practitioners use so many different types of tools, it is a key goal that you can customize the stack to whatever your requirements (within reason), and let the system take care of the "boring stuff." While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.

Microsoft has rolled out the beta release of TypeScript 2.0. Developers can get it after downloading TypeScript 2.0 Beta for Visual Studio 2015, which will require VS 2015 Update 3. This release includes new features like a workflow for getting TypeScript type definition files. "Null and undefined are two of the most common sources of bugs in JavaScript," and before this release, null and undefined were in the domain of every type. "If you had a function that took a string, you couldn't be sure from the type alone of whether you actually had a string--you might actually have null."