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// our training
Learning to Rank
Learning to Rank
[Solr + Elasticsearch]
In this Learning to Rank Training, you will Solve a ranking problem by integrating a 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 and Elasticsearch Integration. Choose the search technology you use.
Public Training sessions
First session
Intro to Learning to Rank - build your training set
This session is a technical guide to exploring and mastering various techniques to generate your training set(s) correctly and efficiently.
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- 6th September 2023 | 5:00 - 7:00 PM GMT+1
Second session
Train, Evaluate and Explain your LTR Model
In this session, you will see the end-to-end pipeline, from the language model upload to its usage in the Neural Search, with real examples and a hands-on tutorial.
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- 13th September 2023 | 5:00 - 7:00 PM GMT+1
Third session
Learning to Rank - Apache Solr Integration
This session illustrates how you can use the Apache Solr LTR component to integrate machine learning with your search pipeline.
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- 20th September 2023 | 5:00 - 7:00 PM GMT+1
Fourth session
Learning to Rank - Elasticsearch Integration
This session illustrates how you can use the Elasticsearch LTR component to integrate machine learning with your search pipeline.
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- 27th September 2023 | 5:00 - 7:00 PM GMT+1
Choose the number of sessions to buy
2 SESSIONS
£ 350,00 By Purchasing the training You Accept our Training’s Terms and Conditions
Buy the first two sessions of this training
• Intro to Learning to Rank - Build your Training Set +
• Train, Evaluate and Explain your LTR model
3 SESSIONS [SOLR]
£ 450,00 By Purchasing the training You Accept our Training’s Terms and Conditions
Buy the first three sessions of this training
• Intro to Learning to Rank - Build your Training Set +
• Train, Evaluate and Explain your LTR model
• LTR - Apache Solr Integration
3 SESSIONS [ELASTIC]
£ 450,00 By Purchasing the training You Accept our Training’s Terms and Conditions
Buy three sessions of this training
• Intro to Learning to Rank - Build your Training Set +
• Train, Evaluate and Explain your LTR model
• LTR - Elasticsearch Integration
Private and recorded training
Pre-recorded [4 sessions]
£ 450,00
If you are not able to attend public training, this is the best option for you. You will be able to take the course at your own pace and rhythm and learn whenever it fits your schedule and mood.
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- Top expert trainers
- Q&A by e-mail
- Certificate of Attendance
Private
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If you are looking for intensive sessions tailored on your (or your team) experience, then private training is your perfect choice!
You can choose between online or live training.
Contact us
- In-presence or Online
- Tailored training
- Top expert trainers
- Certificate of Attendance
Based on experience with leading companies including
feedback from past students
"The training has prepared me well to tackle my own project. It helped me to understand how to set up the project and which tools or algorithms I can use for it.
The content of the training is quite compact, but not overloaded, so that there was also time for individual questions.
I particularly liked the fact that Alessandro shared his experiences from older projects, which allowed him to point out potential problems."
Julia Silberberg
Jobware
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;
• 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.
INTENDED AUDIENCE
• Software Engineers
• Data Scientists
• Machine Learning passionates.
// Learning to Rank
Our Trainers
Alessandro Benedetti
APACHE LUCENE/SOLR COMMITTER
APACHE SOLR PMC MEMBER
Alessandro Benedetti is the founder of Sease Ltd.
Alessandro has been involved from the beginning to the Apache Solr Learning To Rank plugin developments carried over by Bloomberg.
Over the years he has worked in various Learning To Rank projects and solutions including the contribution to Apache Solr of the Interleaving capability.
Anna Ruggero
R&D Search Software Engineer, her focus is on the integration of Information Retrieval systems with advanced Machine Learning, Neural Search models ad Recommender Systems.
She likes to find new solutions that integrate her work as a Search Consultant with the latest academic studies.
Ilaria Petreti
Data Scientist, passionate about the world of Artificial Intelligence. She loves applying Data Mining and Machine Learning techniques to help companies making careful data-driven decisions and increase their efficiency.
Full Program
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