Designing State of the Art Recommender Systems
Learn how to build modern recommender systems in Sphere's live course from those who designed them at Netflix, Pinterest, LinkedIn and Quora.
Access to: session recordings, curated resources and exclusive events
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
Engineering Leaders
Engineering managers, directors, VPs, and CTOs who want to make their organizations data-driven with metrics and A/B tests
Designed for Data Scientists and Machine Learning Engineers
who are looking to build and optimize state of the art recommender systems
- 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
- 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
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?
In traditional software development, CI/CD automates many tasks, including testing, building and deploying software. But CI/CD for ML is a different beast. Testing and deployment of ML can be triggered by many event types, and observability and logging requirements are materially different for ML.Today, no single tool can facilitate end-to-end CI/CD for ML. The process of testing, building and deploying ML requires a symphony of tools and glue code to create an integrated CI/CD system. To offer an entry point that many data scientists and engineers are familiar with, we’ll teach you how to integrate GitHub with other ML tools to build custom CI/CD automations for ML that will increase your engineering efficiency and prevent errors from being released to production.
- 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)
- 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
- 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
- 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
- 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
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Team?
Sphere offers a range of subscription packages that provide discounts on all courses in our library. We help upskill employees at some of the world’s best companies. Learn more about pricing options here or book a time to talk to one of our staff below.
Book a free consultation
Learn live from a world-class
Instructor
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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.
Guest Lectures by
Industry Experts
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!
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.
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.
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.
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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