Machine Learning Powered Search
Apply machine learning to ranking, query and content understanding with Doug Turnbull, one of Reddit's most senior engineers.
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 information retrieval practitioners
hoping to improve NDCG and search-to-conversion through more relevant search results
- Learn the inner workings of machine learning based search relevance ranking
- Create reliable search relevance training data that confidently labels what search results are relevant based on user clicks and other signals
- Explore new ranking features in production to overcome the echo chambers present in existing search interaction data
- Have experience tuning relevance for a ranking system (search, recos, etc) using a metric like NDCG in the past
- Have experience tweaking search engine relevance settings (Elasticsearch, Solr, etc) to achieve some business goal
- Have trained a model in Python with sklearn or similar tool in the past (however simple)
- You’re about to embark on or are already implementing a machine-learning implementation for search
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?
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.
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
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
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
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
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
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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
Learn live from a world-class
Instructor
Since 2012, Doug Turnbull has worked with companies such as Reddit, 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.
Currently, Doug is a principal engineer at Reddit, which he described on LinkedIn as "a career opportunity to rethink how search can have a positive influence on the lives of real people."
Learn live from world-class
Instructors
Since 2012, Doug Turnbull has worked with companies such as Reddit, 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.
Currently, Doug is a principal engineer at Reddit, which he described on LinkedIn as "a career opportunity to rethink how search can have a positive influence on the lives of real people."
Guest Lectures by
Industry Experts
”Taking a course with Doug is one of the best career moves you can make. He is a veritable powerhouse of Search Relevance knowledge, with deep expertise in click models, ranking algorithms, and information retrieval fundamentals. Not only does Doug bring a wealth of experience to his teaching, he also brings levity and creativity. Learning from him is a pleasure not to be missed.”
“I've attended many of Doug's talks, referenced his articles, been a student in his classes (on learning to rank and click models), and even written a book with him. He is able to distill complex search relevance topics into intuitive, fun, and practical examples that make learning from him an invaluable and unforgettable experience.”
"I've been supporting search teams for a decade, and the course has been a nice blend of the familiar and the brain-melting. Perfect for the season I'm in, refreshing my own knowledge about the domain from first principles. I'm already having higher level conversations with clients who are looking to improve the quality of their search results."
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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