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Learning to Rank 2022
April 4 @ 3:00 pm - April 6 @ 7:00 pm
£1200
// our training
Learning to Rank [Solr OR Elasticsearch]
In this Learning to Rank Training you will Solve a ranking problem integrating machine learning system with your search engine. You will learn how to build a training set, train your model and test it both online and offline.
The Learning to Rank Training will cover Apache Solr Integration OR Elasticsearch Integration.
1200 GBP
(if you book before 28/03 price is 840 GBP)
Skills You Will Gain
• How to integrate Machine Learning with your Search Engine to tune your relevance function;
• How to gather user feedback and prepare your training set;
• Ranking models life-cycle (Training and Deploy);
• How to test your ranking models Offline/Online.
Prerequisites
Basic understanding of Search Engines and Machine Learning
Intended Audience
Software Engineers, Data Scientists, Machine Learning passionates.
Our Trainers
Alessandro Benedetti
APACHE LUCENE/SOLR COMMITTER
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.
Schedule
4TH APRIL 2022
03:00 PM – 07:00 PM (GMT+1)
5TH APRIL 2022
03:00 PM – 07:00 PM (GMT+1)
6TH APRIL 2022
03:00 PM – 07:00 PM (GMT+1)
Full Programme
Introduction to LTR
- Offline Learning to Rank Techniques
- Core Concepts
- Algorithms
- State of the art
- Online Learning to Rank
- Core Concepts
- Algorithms
- State of the art
How to Build your Training Set
- Implicit Feedback
- Explicit Feedback
- Feature Engineering
- Feature level
- Feature type
- Categorical Features
- Missing values
- Relevance Label Estimation
- Click Modelling
- Train/Test/Validation Split
- Hands On Exercises
- Categorical Encoding
- Missing Values Count
- From interactions to training set
- Let’s split the training set
How to Train your Model
- Libraries Overview
- Ranklib
- XGBoost
- Hands On Exercises
- Let’s train a model using XGBoost
Evaluation and Explainability
- Offline Model Evaluation
- Metrics
- Open Source Tools
- Online Model Evaluation
- A/B Testing
- Interleaving
- Explain your Model
- Overview
- Open Source Libraries
- Hands On Exercises
- Let’s explain a model using TreeSHAP
Open Source Search Engines Integration
Apache Solr Integration OR Elasticsearch Integration
- Features Management
- Ranking Models Management
- How to rerank search results
- Extract features from the results
- Interleaving (Apache Solr only)
- Hands On Exercises
- Upload Features definition and Models
- Run a re-ranking query
- Interleave two models in the results
- Extract features from the results
War Stories
