Maximize and Extend Your Machine Learning Capabilities with Signals in Fusion
Today, customers expect a great search experience that delivers meaningful results with suggestions of new items/articles to explore based on their preferences. Delivering this experience requires leveraging signals (user-feedback on search application) within your search framework. How do you incorporate signals in a manner that enables you to continuously maximize your return on Machine Learning investments (such as Data Scientists) and what can you do if you are just getting started in AI-powered search?
This session will cover all of your burning questions:
- What information should you collect in your signals framework?
- How can you protect the Personal Identifiable Information of your users?
- What benefits of signals you should already be experiencing?
- What types of recommendations should you expect from signals?
- How can you enable data scientists to explore and consume signals for downstream ML tasks?
- How can you enable data scientists to deploy their models into search pipelines for faster Machine Learning ROI?
You’ll leave this session with specific steps you can take to empower Merchandisers, Search Architects and Data Scientists to make their own data-driven decisions, backed by ML running at scale in the background.
Merchandisers, Search Developers and Architects, Data Scientists, Ecommerce business leaders
Learn how to get the highest business impact from your signals architecture to ensure that you can continue to adapt your search experience to delight your users.