Alexa has a very good Natural Language Processing engine. However, there are other NLP engines in the market that can be used and those are including more and more capabilities.
Here you have the technologies used in this project
Now, we have everything prepared and ready to go to a Kubernetes Cluster in a cloud provider. It is a fact that creating a cluster in any cloud provider manually is a difficult task. Moreover, if we want to automate this deployment, we need something that helps us in this tedious task. In this article, we will see how to create a Kubernetes Cluster and all of its required objects, deploying our Alexa Skill with Terraform using Google Kubernetes Engine.
Here, you have the technologies used in this project:
You can build a custom skill for Alexa by extending a servlet that accepts requests from and sends responses to the Alexa service in the cloud.
This project will walk through how to build an Alexa Skill with Spring Boot and Http Servlet mapping examples.
The usability of the skill directly depends on how well the sample utterances and custom slot values represent real-world language use.
As Alexa best practices say:
Every developer understands there are multiple interfaces for end-users to interact with technology — keyboard, mouse, touch-pad, touch-screen, pointing device, VR, AR, mixed reality, and voice. With the popularity of devices like Amazon Echo, voice-interactive experiences have enhanced how many engage technology within the home or office. In recent months, the Amazon Alexa team announced a new feature that provides access to the geolocation of participating Alexa-enabled devices. What does this mean? It provides an opportunity for end-users to interact with Alexa when away from home and allow custom experiences based on location.
From a developer’s perspective, this could raise multiple questions:
