Recommender System with Elasticsearch: Nick Pentreath & Jean-François Puget

At the recent sold-out Spark & Machine Learning Meetup in Brussels, Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver the main talk of the meetup: Creating an end-to-end Recommender System with Apache Spark and Elasticsearch.

Jean-François and Nick started with a look at the workflow for recommender systems and machine learning, then moved on to data modeling and using Spark ML for collaborative filtering. They closed with a discussion of deploying and scoring the recommender models, including a demo.

See a video of the talk on the Spark Technology Center Youtube channel ...

See the slides on SlideShare

Creating an end-to-end Recommender System with Apache Spark and Elasticsearch - Nick Pentreath & Jean-François Puget

Find Nick and Jean-François on Twitter:


You Might Also Enjoy

Gidon Gershinsky
Gidon Gershinsky
2 months ago

How Alluxio is Accelerating Apache Spark Workloads

Alluxio is fast virtual storage for Big Data. Formerly known as Tachyon, it’s an open-source memory-centric virtual distributed storage system (yes, all that!), offering data access at memory speed and persistence to a reliable storage. This technology accelerates analytic workloads in certain scenarios, but doesn’t offer any performance benefits in other scenarios. The purpose of this blog is to... Read More

James Spyker
James Spyker
4 months ago

Streaming Transformations as Alternatives to ETL

The strategy of extracting, transforming and then loading data (ETL) to create a version of your data optimized for analytics has been around since the 1970s and its challenges are well understood. The time it takes to run an ETL job is dependent on the total data volume so that the time and resource costs rise as an enterprise’s data volume grows. The requirement for analytics databases to be mo... Read More