In a previous blog post I demonstrated how the vector datatype in Elasticsearch can be used to search words by their semantic meaning. In this post I will show how a reverse image search for paintings can be implemented using the same methods. Given a photo of a painting, we will use Elasticsearch to find other paintings which look similar.

AWS is the Amazon’s cloud platform which is full of ready-to-use services. In this entry, we’re going to take a look at one of the services offered by AWS, Rekognition, which is a Machine Learning service that is able to analyse photographs and videos looking for objects, people or text.

Have you ever wondered why IntelliJ can't resolve properties in your application.properties or application.yml? Short answer: you'll need an annotation processor in your classpath.

Full-text search is one of Elasticsearch's strengths. However, what if we wanted to find similar documents based on something more abstract - like the meaning of a word or the style of writing? This is where Elasticsearch's dense vector field datatype and script-score queries for vector fields come into play.

This is the first part of a two-part blog post that is concerned with extracting data from Jira and using it for further applications such as visualization, evaluation, and using the power of machine learning to gain valuable insights in the data.