Design Considerations for Building ML- Powered Search Applications - Mark Moyou

Design Considerations for Building ML- Powered Search Applications - Mark Moyou

Design Considerations for building ML-Powered Search Applications - In this talk, we discuss several ML strategies and design considerations around collecting and using signal data to train ranking algorithms and Collaborative Filtering recommenders as a component of a larger information portal. Recent progress in NLP and deep learning have ushered in a new wave of search methodologies, primarily vector-based search which is distinctly different from lexical-based search engines. We discuss how your businesses can leverage these advancements to drive a better user experience around information retrieval.

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Design Considerations for building ML-Powered Search Applications

slides: https://www.linkedin.com/in/markmoyou/detail/overlay-view/urn:li:fsd_profileTreasuryMedia:(ACoAAAgpb3gBE4kcwqgTVou2-f4RUHcdYcDIpCY,1635452328576)/

ABOUT: Recent shifts to a remote economy have accelerated online commerce activities and the need to access remote information repositories. Information discovery is enabled through Search and Recommendations, but users expect Google-like quality results. In this talk, we discuss several ML strategies and design considerations around collecting and using signal data to train ranking algorithms and Collaborative Filtering recommenders as a component of a larger information portal. Recent progress in NLP and deep learning have ushered in a new wave of search methodologies, primarily vector-based search which is distinctly different from lexical-based search engines. We discuss how your businesses can leverage these advancements to drive a better user experience around information retrieval.

About the speaker: Mark Moyou, PhD is currently a Senior Data Scientist at NVIDIA. Prior to NVIDIA, he was Data Science Manager at Lucidworks, a hyper-growth, San-Francisco based startup that delivers ML-powered search where he worked with Fortune 1000 clients on their Search and Recommendations roadmap for powering both external customer-facing and internal-facing search applications.

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