Presented at virtual Activate 2020. Maximizing business value in a digital environment where search queries are the primary view onto your user’s needs dictates that Search is a Machine Learning problem. But how much “ML” is really needed these days, and what kind of infrastructure is required to support all this ML?
In this talk, Jake discusses a short list of what is required (in terms of product feature, architectural component, and engineering technique) for our search engines to get a “seat at the table” of our user’s highly divided attention.
Jake Mannix, Search Relevance Architect, Salesforce.com, Inc.
For some, these will be reminders that yes: you need a Feature Store, personalization, session history, and the like. For those new to ML-in-Search, it’ll be short list of places to start (but it might be 3-5 years before you’ve added it all!)
Engineers, Data Scientists, and Product Managers for Search teams, Executives, and Managers making Search Engine tech purchasing decisions