Search for Product Managers
Building effective search applications requires addressing a number of technical challenges. But fundamentally search is a product challenge, and the best search applications have strong product managers who deeply understand search.
This course is designed to teach product managers the most important things they need to know about search: Metrics and Evaluation, Relevance and Ranking, Content and Query Understanding, and Vectors and Neural Search.
Course taught by expert instructors
Machine Learning Consultant
Daniel is an independent consultant specializing in search, machine learning / AI, and data science. He was a founding employee of Endeca, a search pioneer that Oracle acquired in 2011. He then led engineering and data science teams at Google and LinkedIn. He’s worked with a wide range of consulting clients, including Apple, eBay, Pinterest, Salesforce, Yelp, and Zoom. He wrote a book on Faceted Search, published by Morgan & Claypool, and he blogs on Medium about search-related topics — particularly query understanding. Daniel has degrees in Computer Science and Math from MIT and a PhD in computer science from CMU.
Learn and apply skills with real-world projects.
This course is for product managers who work with or want to work with search applications.
The class assumes no previous knowledge of search.
Try these prep courses first
- Defining Relevance: Precision, Recall, Position, Discounted Cumulative Gain (DCG)
- Collecting Judgements: Explicit Human Judgements vs. Implicit Behavioral Judgements
- Metrics: Query vs. Session vs. User, Components vs. End-to-End, User vs. Business
- Experimentation: Offline Analysis, A/B Testing, Interleaving, Explore-Exploit
- Quantitative vs. Qualitative Evaluation: Analyzing Data vs. Conducting User Studies
- Relevance vs. Ranking: Separating Objective and Subjective Concerns
- Ranking Factors: Query-Dependent vs. Query-Independent vs. Contextual
- Hand-Tuned and Machine Learning Ranking: Theory and Practice
- Multiple-Phase Ranking: Computational Tradeoffs and Multiple Objectives
- Limitations: When Ranking is Not the Solution to Your Search Problem
- Matching vs. Ranking: Simplifying the Problem by Factoring It
- Content Understanding: Indexing Content to make it Findable
- Query Understanding: Representing Queries as Search Intents
- Hand-Tuned and Machine Learning Content and Query Understanding
- Limitations: When to Trust Algorithms vs. When to Ask Humans
- AI-Powered Search: Representing Content and Queries as Vectors
- Semantic Retrieval: Nearest Neighbors and Approximate Methods
- Ranking: Combining Vector Similarity with Other Factors
- Filtering and Sorting: Challenges of Similarity-Based Retrieval
- Limitations: When to Stick with Traditional Indexing Representations
Work on projects that bring your learning to life.
Made to be directly applicable in your work.
Live access to experts
Sessions and Q&As with our expert instructors, along with real-world projects.
Network & community
Core reviews a study groups. Share experiences and learn alongside a global network of professionals.
Support & accountability
We have a system in place to make sure you complete the course, and to help nudge you along the way.
Course success stories
Learn together and share experiences with other industry professionals
If you’re a Product Manager in a Search or a Search adjacent space then you don’t want to miss Daniel’s course on Search for Product Managers. I’ve worked with Daniel at LinkedIn and have continued to rely on him for advice on building Search products in my subsequent roles. He has unmatched expertise in both the product thinking and technology that goes into building a world-class Search experience. If you’re building either a domain specific or general search product you’ll get a ton of value from this course.