Online Search Quality Evaluation With Kibana – Queries in Common
This ‘tips and tricks’ describes the steps to follow to compare models on common queries in online search quality evaluation
Ilaria Petreti
June 19, 2023
How to Import Pandas in AWS Lambda
In this blog post, you will learn how to import the Pandas library in AWS Lambda in order to execute python scripts.
Ilaria Petreti
November 7, 2022
How to Deal with Too Many ‘object’ in Pandas from JSON Parsing
We propose and test a way to manage categorical data during the collection and store it directly as numeric types in the JSON.
Ilaria Petreti
March 30, 2022
Efficiently Manage Numeric Ids in JSON and Pandas
In this blog post we make an experimental analysis to identify the best data type to use when dealing with ids.
Anna Ruggero
March 17, 2022
How to manage large JSON efficiently and quickly: multiple files
Tips and tricks to find out efficient and fast ways to read and parse a big JSON file in Python using real-world application
Ilaria Petreti
November 25, 2021
How to manage large JSON files in Python efficiently and quickly
Tips and tricks to find out efficient and fast ways to read and parse a big JSON file in Python using real-world application
Ilaria Petreti
October 13, 2021
Drop constant features: a real-world Learning To Rank scenario
Does removing constant features affect model performance? Find out with our real-world Learning to Rank application
Ilaria Petreti
June 16, 2021
The luceneMatchVersion Parameter in Apache Solr
This blog explores how the luceneMatchVersion parameter in the solrconfig.xml works in Apache Solr. Dos and don’ts, and anything in between.
Alessandro Benedetti
April 21, 2021
Common Errors with Apache Solr Feature and Model Stores
Common errors and warnings in manipulating feature stores and models in Solr. Pay attention also to JVM Heap and Zookeeper.
Anna Ruggero
March 8, 2021
Apache Solr Learning to Rank Feature Stores and Models
How to list, upload, delete feature stores and models necessary in Solr for learning to rank.
Anna Ruggero
February 25, 2021
We are Sease, an Information Retrieval Company based in London, focused on providing R&D project guidance and implementation, Search consulting services, Training, and Search solutions using open source software like Apache Lucene/Solr, Elasticsearch, OpenSearch and Vespa.
