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Personalized Recommendations at Scale

Surfacing relevant content from among millions of candidates to users in real-time is a challenging task addressed by recommender systems. Most modern-day recommender systems rely on the complex interplay between different components, each of which is powered by sophisticated machine learning algorithms. In this course, we provide a holistic overview of ML modeling choices that go into developing and deploying multi-stage recommenders capable of serving recommendations from hundreds of million content choices to multiple hundred million users. The course goes into algorithmic models that power the various stages of the recommender, including the candidate generator, core ranker, user representation learning modules and offline and online evaluation module. The course ends with case studies, lessons and practical considerations from deployed systems powering over 400 million users.

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Rishabh Mehrotra
Director of Machine Learning at ShareChat
Price
US$ 500
or included with membership
Duration
4 weeks
Space is limited

Course taught by expert instructors

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Rishabh Mehrotra

Director of Machine Learning at ShareChat

Rishabh Mehrotra currently works as a Director of Machine Learning at ShareChat based in London. His current research focuses on machine learning for marketplaces, multi-objective modeling of recommenders, and the creator ecosystem. Prior to ShareChat, he was an Area Tech Lead and Staff Scientist/Engineer at Spotify where he led multiple ML projects from basic research to production across 400+ million users. Rishabh has a PhD in Machine Learning from UCL, and 50+ research papers and patents. Some of his recent work has been published at conferences including KDD, WWW, SIGIR, RecSys, and WSDM. He has co-taught a number of tutorials and summer school courses on the topics of learning from user interactions, marketplaces, and personalization.

The course

Learn and apply skills with real-world projects.

Who is it for?
  • Industry practitioners tasked with developing and deploying a large scale recommender system

  • MLEs with prior machine learning experience looking into diving deep into large scale recommender systems

Prerequisites
  • Some familiarity with basic machine learning concepts like model training, feature representations, labels

  • Ability to write Python and work with documented libraries

Not ready?

Try these prep courses first

Learn
  • Recommendation Problem Formulation
  • Multi-stage Recommender System
  • ML Approaches for Generating Recommendations
  • Modern Recommender Systems
Implement 1-3 candidate generators, from simple recall-based CGs to noise contrastive estimator-based CGs. Conduct recall-based evaluation of the different approaches.
Learn
  • Recommendation Rankers
  • Contextual Bandits for Recommendations
  • Playlist Recommendation Model
Implement a multi-task recommender, predicting multiple user engagement signals, and combining various predictions to serve the top-k recommendations. Perform various ranking and user engagement-based evaluations.
Learn
  • Learning User Representations
  • Topical Representation of Users
  • Learning About Users
Implement two user representation techniques and compare their performance on downstream recommendation tasks.
Learn
  • Beyond AUC: Offline Evaluation Setup, Session Level Metrics, Implicit Signals Logged Data
  • Nuances of Online Experimentation: AB Test Design, and Online Metrics
  • Offline-online Correlation: Scaling
  • Counterfactual Evaluation: Randomized Data Collection, Unbiased Estimators of Metrics
Using logged data of user interactions, implement a few session-based metrics, and identify the biases of the metric.

A course you'll actually complete. AI-powered learning that drives results.

AI-powered learning

Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.

Live courses by leading experts

Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.

Practice on the cutting edge

Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.

Flexible schedule for busy professionals

We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.

Timeline

Completion certificates

Each course comes with a certificate for learners to add to their resume.

Best-in-class outcomes

15-20x engagement compared to async courses

Support & accountability

You are never alone, we provide support throughout the course.

Get reimbursed by your company

More than half of learners get their Courses and Memberships reimbursed by their company.

Hundreds of companies have dedicated L&D and education budgets that have covered the costs.

Reimbursement

Course success stories

Learn together and share experiences with other industry professionals

This is my second course from Uplimit and I am really happy with the number of new things I learned. The material was quite in-depth and the projects were rather challenging but quite fulfilling. A lot of material on recommenders can really only be found in research papers and the team at Uplimit has managed to condense a lot of that into a 4-week course, which you could never get anywhere else.

Yudhiesh RavindranathData Scientist, MoneyLion

It's been amazing to learn from an industry expert in RecSys himself. Rishabh and Uplimit team structured the course in such a way that the salient details are covered really well. The pragmatic touch through projects was a cherry on top! I would definitely suggest anyone who has an interest in implementing to the deploying their recommender systems at scale to take this course!

Tanya Khanna

Rishabh is an expert in recommendations and you can feel his passion for the field throughout the course. We covered some of the hottest aspects of recommendations nowadays in a very hands-on manner 🙂.

Mathieu Sibué

If you have some experience with Recommender Systems, you will find Personalized Recommendations at Scale taking you to the next level. The course introduces you to various concepts relevant to industrial systems and it will help you understand them through practical exercises at the end of each week. The community is constructive and you will find many experienced members helping you throughout the projects and make good connections while you learn and have fun.

Himanshu Maurya

Great course that was approachable enough for most practitioners while still getting deep into the weeds about state-of-the-art ongoings in recommendation.

Maxwell CunhaData Scientist at ASICS Digital

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