// Learning To Rank training
Train, Evaluate And Explain Your Learning To Rank Model
In this training, you are going to learn how to train, evaluate, and interpret models, exploring essential libraries and strategies for impactful ranking decisions. Engage in hands-on code examples for a deeper understanding of the process.
Recorded
£ 150,00
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.
- Top expert trainers
- Q&A by e-mail
- Certificate of Attendance
Public classroom
£ 250,00
SCHEDULE
7th March 2024
3:00 – 7:00 PM GMT
- Live on Zoom
- Top expert trainers
- Certificate of Attendance
Private
ask for quote
If you are looking for intensive sessions tailored to your (or your team’s) experience, then private training is your perfect choice!
- In presence or Online
- Tailored Training
- Top Expert Trainers
- Certificate of Attendance
Recorded
£ 150,00
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.
- Top expert trainers
- Q&A by e-mail
- Certificate of Attendance
Private
ask for quote
If you are looking for intensive sessions tailored to your (or your team’s) experience, then private training is your perfect choice!
- In presence or Online
- Tailored Training
- Top Expert Trainers
- Certificate of Attendance
Based on experience with leading companies including
Based on experience with leading companies including
PREREQUISITES
• Basic understanding of Search Engines and Machine Learning
• Theory behind the training and test set creation for learning to rank.
WHAT YOU WILL LEARN
• Which are the most common learning to rank libraries
• How offline testing works for business
• How to practically create a training and test set
• How to train and evaluate a model
• How online testing works for business
• How to explain your learning to rank model with SHAP
INTENDED AUDIENCE
• Data scientists
• Software Engineers
• Developers
Your Trainers
Alessandro Benedetti
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.
Anna Ruggero
R&D Search Software Engineer, her focus is on the integration of Information Retrieval systems with advanced Machine Learning, Natural Language Processing and Data Mining algorithms. She likes to find new solutions that integrate her work as a Search Consultant with the latest Academia studies.
The schedule
The training session is meticulously structured for optimal comprehension and retention.
It commences with a comprehensive 1-hour theory session, followed by a brief 15-minute test to assess understanding. Subsequently, participants engage in a 45-minute Q&A segment, fostering interactive learning and clarification of concepts. The training then resumes with another 1-hour theory module, another 15-minute evaluation test, and concludes with a final 45-minute Q&A session to consolidate knowledge and address any remaining queries.
3:00-4:00 theory session
4:00-4:15 test
4:15-5:00 Q&A segment
5:00-6:00 theory session
6:00-6:15 test
6:15-7:00 Q&A segment
Topics
1. Available and most common learning to rank libraries and approaches
Explore a comprehensive overview of the diverse Learning to Rank (LTR) libraries and approaches widely used in the industry. Understand the strengths, limitations, and specific use cases associated with each library. Gain insights into the landscape of LTR methodologies, laying the foundation for informed decision-making in selecting the most suitable approach for your business needs.
2. Offline Testing for Business
Delve into the critical aspect of offline testing for business applications. Learn the methodologies and best practices for evaluating LTR models offline, ensuring robustness and effectiveness in simulated scenarios. Understand the significance of offline testing metrics and how they align with real-world business objectives, providing a solid foundation for model refinement and optimization.
3. Build a Training and Test Set with Practical Code
In this section, we guide you through code demonstrations on effectively splitting your dataset into training and test sets for model training. While we won’t delve into dataset construction, you’ll gain hands-on experience in the crucial step of partitioning data for optimal model preparation.
4. Model Evaluation Metrics
Explore a variety of model evaluation metrics used in assessing the performance of LTR models. Dive into the nuances of precision, recall, NDCG, and other key metrics, understanding how they reflect different aspects of ranking quality. Acquire the skills to interpret and choose appropriate evaluation metrics based on specific business goals, ensuring accurate and meaningful model assessment.
5. Train and Test the Model with Practical Code
Roll up your sleeves and engage in hands-on training to build, train, and test LTR models. Follow practical code examples that illustrate the implementation of popular algorithms and frameworks. Develop proficiency in the end-to-end process of model development, from training to testing, empowering you to apply these skills to real-world business scenarios.
6. Common Mistakes
Navigate through common pitfalls and challenges encountered in LTR projects. Identify and understand mistakes that can impact the effectiveness of your models. Equip yourself with strategies to troubleshoot issues, optimize model performance, and enhance the overall success of your LTR implementation.
7. Online Testing for Business
Extend your knowledge to the realm of online testing for LTR models in business contexts. Explore methodologies and strategies for conducting online evaluations, leveraging real-time user interactions to assess model performance dynamically. Understand the implications of online testing on user experience and business outcomes.
8. Model Explainability Exploiting the SHAP Library with Practical Code:
Uncover the importance of model explainability in LTR and learn how to leverage the SHAP (SHapley Additive exPlanations) library to interpret and explain model decisions. Walk through practical code examples that demonstrate the application of SHAP values, providing transparency into the factors influencing ranking outcomes. Enhance your ability to communicate model insights effectively within your business context.
FAQ
The Learning To Rank training about Train, Evaluate and Explain your Learning to Rank Model will take place live on zoom!
We are working on being able to provide a recording of the training for those interested.
Everyone can participate on this training, the only prerequisites are written just over there!
Learning To Rank training about Train, Evaluate and Explain your Learning to Rank Model last around 2 hours, with 20 minutes of Q&A.
Yes, the training includes a 20 minutes Q&A!
Your teachers will be:
– Alessandro Benedetti, Apache Lucene/Solr committer and Apache Solr PMC member.
– Anna Ruggero, R&D Software Engineer
Sure, at the end of the training you will receive a badge of attendance by e-mail.
If you need it, you can consult our Training’s Terms and Conditions.
Yes, you can contact us and find the best option for you and your team!