With the Learning To Rank (or LTR for short) plugin, you can configure and run machine-learned ranking models in Elasticsearch. The module also supports feature extraction inside Elasticsearch. The only thing you need to do outside Elasticsearch is to train your own ranking model. This training illustrates how you can use the Elasticsearch LTR component to integrate machine learning with your search pipeline.
PREREQUISITES
• Basic understanding of Search Engines and Machine Learning.
WHAT YOU WILL LEARN
• How to integrate Machine Learning with your Search Engine to tune your relevance function;
• Ranking models life-cycle (Training and Deploy);
• How to test your ranking models Offline/Online.
• How to Build your Elasticsearch LTR Query
APACHE LUCENE/SOLR COMMITTER APACHE SOLR PMC MEMBER
Alessandro has been involved in designing and developing search-relevant solutions from 2010. Over the years he has worked on various projects, with various open source technologies aiming to build search solutions able to satisfy the user information needs, often integrating such solutions with machine learning and artificial intelligence technologies.
Andrea Gazzarini
RRE CREATOR
RRE Creator Andrea Gazzarini is a curious software engineer, mainly focused on the Java language and Search technologies. With more than 15 years of experience in various software engineering areas, his adventure in the search world began in 2010, when he met Apache Solr and later Elasticsearch.
The Learning To Rank Elasticsearch Integration trainings will take place live on zoom! We are working on being able to provide a recording of the training for those interested.
Your teachers will be: – Alessandro Benedetti, Apache Lucene/Solr committer and Apache Solr PMC member. – Andrea Gazzarini, RRE Creator with more than 15 years of experience in various software engineering areas.
If you can’t attend last minute you can contact us and reschedule on a different date with our team. Bear in mind this will have to be a private training.