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Designed to help make organizations

Data Driven

Product Leaders

Program managers and product managers that are focused on metrics like growth and revenue, and prioritization decisions

Data Scientists

Data Scientists and Data Science managers who help map strategic decisions to actionable experimental designs and then interpret the results in a trustworthy manner

The motivation and basics of A/B testing (e.g., causality, surprising examples, metrics, interpreting results, trust and pitfalls, Twyman’s law, A/A tests)
Cultural challenges, humbling results (e.g., failing often, pivoting, iterating), experimentation platform, institutional memory and meta-analysis, ethics
Hierarchy of evidence, Expected Value of Information (EVI), complementary techniques, risks in observational causal studies

Engineering Leaders

Engineering managers, directors, VPs, and CTOs who want to make their organizations data-driven with metrics and A/B tests

Safe deployments
Triggering, especially in evaluating machine learning models
The benefits of agile product development

Designed for Data Scientists and Machine Learning Engineers

who are looking to build and optimize state of the art recommender systems

You will:
  • Gain insights into how state-of-the-art recommender systems are designed and deployed, which can be applied at both early- and late-stage companies
  • Think critically about the possible approaches to designing a recommender system and the pros/cons of each
  • Propose a credible roadmap to introduce (or improve) recommender systems into complex products
You should:
  • Have 3+ years of experience in either product OR engineering roles (e.g., Staff Engineer, Senior Technical PM or higher)
  • Have experience creating and/or working with machine learning algorithms in industry settings
  • Have a basic, high-level understanding of how a machine learning system is built — including data collection, model training and evaluation, deployment, and monitoring

Learn live from a world-class

Instructor

Learn live from a world-class

Instructor

Deepak Agarwal (Ph.D) is VP of engineering at Pinterest, where he leads all engineering that power user experiences on the Pinterest app and browser. Prior to this, he was VP of AI at LinkedIn, where he established an AI/ML org and scaled it from 2 to 500+ in 8 years. He began his technical career in recommender systems at Yahoo! Research and deployed one of the first recommender systems for web applications personalizing the Yahoo! front page with an improvement of 300% over 2 years. He has over 100+ research publications, 6000+ citations, 50+ patents (issued) and has published a book on Recommender Systems for practitioners. He began his career as a statistician and is a Fellow of the American Statistical Association.

Learn live from a world-class

Instructor

Learn live from a world-class

Instructor

Xavier Amatriain (Ph.D.) is VP of AI Product Strategy at LinkedIn, where he works at the intersection of what he loves most: user-facing product impact and deep AI technology. Xavier is also a co-founder of Curai, a series B health tech startup, where he was CTO. Previous to this, he led Engineering at Quora and was Research/Engineering Director at Netflix, where he started and led the Algorithms team building the famous Netflix recommendations. Prior to this, he was a researcher both in academia and industry. With over 100 research publications (and 5k citations), Xavier is best known for his work on AI and machine learning in general, and recommender systems in particular.

Learn live from world-class

Instructors

Deepak Agarwal (Ph.D) is VP of engineering at Pinterest, where he leads all engineering that power user experiences on the Pinterest app and browser. Prior to this, he was VP of AI at LinkedIn, where he established an AI/ML org and scaled it from 2 to 500+ in 8 years. He began his technical career in recommender systems at Yahoo! Research and deployed one of the first recommender systems for web applications personalizing the Yahoo! front page with an improvement of 300% over 2 years. He has over 100+ research publications, 6000+ citations, 50+ patents (issued) and has published a book on Recommender Systems for practitioners. He began his career as a statistician and is a Fellow of the American Statistical Association.

Xavier Amatriain (Ph.D.) is VP of AI Product Strategy at LinkedIn, where he works at the intersection of what he loves most: user-facing product impact and deep AI technology. Xavier is also a co-founder of Curai, a series B health tech startup, where he was CTO. Previous to this, he led Engineering at Quora and was Research/Engineering Director at Netflix, where he started and led the Algorithms team building the famous Netflix recommendations. Prior to this, he was a researcher both in academia and industry. With over 100 research publications (and 5k citations), Xavier is best known for his work on AI and machine learning in general, and recommender systems in particular.

Recommended by

Industry Experts

Daniel Tunkelang
Former Director of Data Science and Engineering @ LinkedIn

Xavier and Deepak are two of the world's top experts on recommender systems. As leaders at Netflix, Pinterest, LinkedIn, and Quora, they have not only pushed the frontiers of both research and execution, but also advanced the field through numerous contributions to the academic and industry community. What an incredible opportunity to learn from the best!

Ronny Kohavi
Former Vice President and Technical Fellow @ Airbnb

Deepak and Xavier are two world-experts in recommender systems with unparalleled experience from multiple companies, where they have had done research, led engineering teams, and made a significant impact.

Expense
the cost

90% of our learners expense the full cost of our courses to their employer. This includes leading startups and enterprises alike.

Check Expense Approval At Your Company

Exclusive Content

to advance your business

Get access to exclusive content through live sessions, meetups and our Student Portal (even after you finish the cohort). Ask questions and get personal feedback directly from your instructors and others taking the course.

Join a diverse and experienced

Community

This cohort gives you access to a rich community of like-minded professionals from some of the best businesses in the world. Even after the course ends, you will continue to learn and build with each other.

About Xavier & Deepak's

Live Cohort

Recommender systems and personalization have become essential components of successful digital products in recent years. Some authors even claim that we have transitioned from the “age of search” to the “age of recommendation” (Chris Anderson in “The Long Tail”). But, designing recommender systems in practice is not trivial. It requires not only understanding of state-of-the-art machine learning approaches, but also engineering (particularly for distributed systems), product design, experimentation and business analytics. In this course we will teach you how to think about all these aspects in order to understand the tradeoffs of different approaches and make the right decision for your application and business.

  • What are important considerations for recommender systems infrastructure in order to deploy a recommender system at scale?
  • How can I overcome the key challenges and opportunities ahead: ethical issues underlying modern day recommender systems and technical approaches, balancing personalization and privacy, and AI-based content generation with human in the loop?

Session 1: Introduction to Recommender Systems: Basics and Classic Techniques

Wednesday, November 2, 2022
8-10 am PST
  • Develop an understanding of the fundamental components and techniques behind modern recommender systems
  • Design a basic recommender system using classic approaches such as collaborative filtering or content based
  • Contrast classic approaches to recommendation by listing their pros and cons (e.g., cold-start)

Session 2: Learning to Rank and Hybrid Approaches to Recommendation

Monday, November 7, 2022
8-10 am PST
  • Develop an intuition of how ranking approaches work in practice.
  • Design a recommender system based on learning to rank approaches.
  • Use hybridization techniques to combine any of the approaches introduced so far

Session 3: Other Advanced Approaches to Recommending Content

Wednesday, November 9, 2022
8-10 am PST
  • Introduce the explore/exploit dilemma in recommender systems and select the appropriate exploration/exploitation tradeoff by understanding multi-armed bandits and similar approaches.
  • Introduce contextual aspects/features to the recommender system by selecting the appropriate modeling approach.
  • Use deep learning for complex recommendations such as sequences, image, or text.

Session 4: Recommender Systems in Industry I

Monday, November 14, 2022
8-10 am PST
  • Contrast and differentiate the approaches to recommendation used at LinkedIn and Pinterest
  • Understand and argue about the importance of ethics and fairness when designing a recommender system
  • Build an intuition for deep learning approaches to recommender systems that will be described in later sessions

Session 5: Recommender Systems in Industry II

Wednesday, November 16, 2022
8-10 am PST
  • Contrast and differentiate the approaches to recommendation used at Netflix and Quora
  • Understand the role of UI components such as explanations in the design of a recommender system
  • Design an end-to-end recommender system infrastructure, from training to serving model results in real-time

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, Sphere 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!

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Learning Experience