Designed for engineers & product managers looking to benefit from

Reliable & Scalable Search

Software and ML Engineers

Software and ML Engineers focused in building, scaling and optimizing search systems

Understand and evaluate key alternatives for information retrieval and search systems
Build, deploy and scale reliable ranking functions for information retrieval
Assess and optimize the quality of search engines using industry leading methods

Technical Product Managers

Technical Product Managers leading teams focused on deploying safe, reliable and scalable search technology

Assess different types of search systems and whether they fulfill product needs
Develop product roadmaps for implementing or optimizing search systems
Evaluate the health and quality of your search systems in comparison to industry standards

Meet Your


Professors Ricardo Baeza-Yates and Fabrizio Silvestri

Ricardo Baeza-Yates is Director of Research at the Institute for Experiential AI of Northeastern University. Previously he served as the CTO of NTENT, a global semantic search leader,  and VP of Research at Yahoo Labs. Ricardo is also a co-author of the best-selling textbook, Modern Information Retrieval which is the most used and cited book on search concepts and technology. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. His areas of expertise are web search and data mining, information retrieval, bias and ethics on AI, data science and algorithms in general.

Fabrizio Silvestri is a Full Professor of Artificial Intelligence at the University of Rome, La Sapienza. Before becoming a Professor, Fabrizio worked as a Principal Research Scientist at Facebook (now Meta) AI where he directed research groups implementing AI technology in Facebook’s products. He is the author of more than 150 papers in international journals and conference proceedings on Web Search and Machine Learning. He is the holder of the "test-of-time" award at the ECIR 2018 conference for an article published in 2007.



About Ricardo & Fabrizio’s

Live Cohort

Web search is a pervasive technology. The most famous, and successful, example of a search technology in practice is Google. Today, it is almost impossible to think of a world without Google or other large search engines. Search, though, it’s not just Google; recommender systems, voice assistants, and smart glasses are just a sample of products whose technology is based on search. Also, Search (as many other fields) is in the process of being revolutionized by Deep Learning and AI applications in general.

The goal of this course is to give to the attendees a technical knowledge of the main algorithms used in a web search engine and to explain how those algorithms are combined and form what is the main skeleton of a search engine system. Together we will review a selection of the main techniques in indexing, ranking, and evaluation. The format of the course will consist of theory mixed with case studies that will put what we learn into action. The instructors will also share their personal experiences in industry to ultimately answer the following key questions:

How can search engines find relevant results from a corpus of billions of documents in such a small amount of time?

Sometimes, it seems search engines read our minds. How’s that even possible?

How are search engine systems changing given the impressive progress AI has made in these last years?

Is search a solved problem? What is next for Search?

Session 1 - ​​Welcome to your cohort

Tuesday, May 10th
8-10am PST

In this session, you will be introduced to your instructors and peers. We will then align on the fundamentals of search theory that will serve as the foundation for upcoming sessions:

Query Relevance

Search Architecture

Difficulties as the data scales

Session 2 - Increasing efficiency using machine learning

Thursday, May 12th
8-10am PST

In this session, we explore the latest methods for increasing search efficiency:

Predicting query types

Predicting index tier 

Predicting query intention

Query completion

Session 3 - Ranking using machine learning

Tuesday, May 17th
8-10am PST

In this session, we show how and where gradient boosting decision trees can be used to improve search ranking:

Positioning search as a classification problem

Gradient boosting decision trees for ranking

Session 4 - Ranking using deep learning

Thursday, May 19th
8-10am PST

In this session, we review how and where the transformer architecture can be used to improve search ranking:

Transformer architecture: BERT

Neural IR: Deep Learning meets Search

Neural IR through pyterrier

Still have questions?

We’re here to help!

Do I have to attend all of the sessions live in real-time?

You don’t! We record every live session in the cohort and make each recording and the session slides available on our portal for you to access anytime.

Will I receive a certificate upon completion?

Each learner receives a certificate of completion, which is sent to you upon completion of the cohort (along with access to our Alumni portal!). Additionally, ScholarSite is listed as a school on LinkedIn so you can display your certificate in the Education section of your profile.!

Is there homework?

Throughout the cohort, there may be take-home questions that pertain to subsequent sessions. These are optional, but allow you to engage more with the instructor and other cohort members!

Can I get the course fee reimbursed by my company?

While we cannot guarantee that your company will cover the cost of the cohort, we are accredited by the Continuing Professional Development (CPD) Standards Office, meaning many of our learners are able to expense the course via their company or team’s L&D budget. We even provide an email template you can use to request approval.

I have more questions, how can I get in touch?

Please reach out to us via our Contact Form with any questions. We’re here to help!

Book a time to talk with the ScholarSite team