With the arrival of Liferay DXP and JAX-RS services, we discovered a whole new world beyond JSON-WS and Service Builder. In this article, we are going to convince you to take the plunge with this new technology, showing how its combination with Swagger and the OpenAPI specification can help us to quickly build robust REST API.

The main goal of using the machine learning tool available with the elastic stack is extracting the maximum value from the information we index. Thus far, we've been able to obtain page views with real time searches or perform data aggregations, but now machine learning allows us to address more particular behaviour and have more specific visualizations using learning techniques.

One of the goals I set for this year is to explore Machine Learning (ML), so after having done a couple of courses here and there, I decided to do a -rather simple- starting project, where I could deal with some of the basic stages of the ML: Get the data, prepare it, choose a model, train it, evaluate it, export it, and make the predictions available for use. For this first project, I chose:

In March 2018 Java 10 was released. But a lot of projects haven't adopted even Java 9. I hope this article will help you figure out whether you should try recent releases or stay with current.

In this blog we will demonstrate, how to integrate two interactive report resources into a web application environment like Liferay. There are some benefits to do so like gathering all reports from different servers into a portal to be accessed as well as leveraging Liferay governance controls on them.