This course is designed for

Search Relevance Engineers and Data Scientists

Relevance Engineers

who tune search engines to create more relevant results

Create ranking solutions that maximize conversion and clicks
Improve problem search queries using machine learning
Avoid common relevance pitfalls, by studying projects from companies like Snag and Wikimedia Foundation

Data Scientists

who measure or evaluate search quality

Measure search quality with clicks and purchases
Tie search measures to business outcomes

Watch Doug's recent live chat on

What's Next in Search

Meet Your


Doug Turnbull

Since 2010, Doug Turnbull has worked with companies such as Shopify, Careerbuilder, Wikimedia Foundation, Shipt, and LexisNexis on improving search experiences. Doug coaches and develops search teams from startups to Fortune 500 companies, setting up organizational practices to build relevant search. Doug wrote “Relevant Search” (2016) and co-authored “AI Powered Search” (2022). He also co-created the Elasticsearch Learning to Rank, bringing machine learning to the most popular search engine, which revamped Wikipedia and Yelp, driving intelligent search behind dozens of companies' search experiences.

Doug currently leads the search relevance practice at Shopify, helping democratize e-commerce discovery for millions of independent merchants.

Want to get to know Professor Bernstein?

You’re invited to our live course information session

Professor Berstein is hosting a live 30 minute session free to attend for all. He will be giving a breif introduction to the his upcoming course and then answering audience questions.

4:00-4:30pm PST; March 15

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About Doug's

Live Cohort

With limited patience — and, often, small screens — many users avoid navigation altogether. Instead, they jump to a search bar to find what they need. Yet, with limited screen real-estate, many teams know search optimization can go bad. You can show only a handful of results out of millions to click on.

How can you deliver the magic your users seek with a conversational, relevant search experience? How do you give users the results that will achieve their goals? This course helps you analyze search behaviors to target relevance treatments, solving them using modern data science and machine learning.

Learn how your business can meet modern users where and how they expect to use applications.

How do I analyze search behaviors to arrive at reliable search relevance training data?

How do I train a ranking model to optimize for relevance?

What search measures correspond to my business needs?

What does a good search experience look like?

How do I explore novel ranking features?

Session 1  - The search relevance problem

October 11th
1 pm (PST)

Tune a relevance solution using traditional, non-ML methods. Upon completing session 1, learners will be able to:

Differentiate relevant from irrelevant results

Develop measures for ranking quality across many searches

Experiment with manually tweaking a relevance solution

Session 2 - Now we train!

October 13th
1 pm (PST)

Train a simple ranking model using hand-labeled results from session 1. At the end of this session, learners will be able to:

Build models that categorize relevant from irrelevant results

Test ranking models with a test-train split

Session 3 - Better training data

October 20th
1 pm (PST)

Overcome core biases in search behavioral data to build a successful ranking model. After this session, learners will:

Overcome core biases in search behavioral data to build a successful ranking model.

Be able to contrast approaches to overcoming biases

Session 4 - Feature discovery

October 25th
1 pm (PST)

Navigate echo-chambers by experimenting with novel ranking features. Learners who complete this session will be able to:

Understand how and why echo chambers occur in search

Obliterate echo chambers underlying search experiences to make positive step-changes in search relevance

Evaluate new query, user, or document ranking features that go beyond your current search solution using Active Learning

Session 5 - Traction

October 27th
1 pm (PST)

The playbook on how to get unstuck from the search relevance problem. Upon completion of this session, learners will be able to:

Manage rollout of search relevance experiments in search

Balance near-term search wins, with longer-term investments needed to take search to the next level

Convince team and stakeholders that your ML-Powered search team is moving in the right direction

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!

Book a time to talk with the Sphere team