Leveraging Machine Learning in Enterprise Search
Deploying machine learning onto enterprise search required an entire team of data scientists, developers and database experts to manage the complex machine learning algorithms and deploy them in search systems. Until now.
New advancements in machine learning are making enterprise search more intelligent – and cost-effective – than ever before by taking the pain out of delivering a more relevant experience for users. This talk will cover how machine learning removes the complexity from managing enterprise search, provides a more effective user experience automatically, and plays a major role in effectively scaling your search practice.
Presenters from Coveo
Gauthier Robe, vice president of platform at Coveo™ will explain the technical aspects of machine learning within Coveo™ Machine Learning (Coveo™ ML)
Daniel Cadoch, partner manager at Coveo™, will discuss the journey to relevance with machine learning for companies with the Coveo™ Relevance Maturity Model.
Using Learning to Rank in ElasticSearch for the Job Market
Jason will walk us through what exactly Learning to Rank is, how the plugin for elasticsearch works, as well as some real life lessons learned from changing the Snagajob search platform to migrate away from full reliance on the vector space model for information retrieval and into machine learned ranking and personalization.
Presenters from Snagajob
Jason Kowalewski, Senior Director of Engineering at Snagajob — The largest marketplace for hourly work in the US — leads the effort to build a new search and recommendation platform that will better match the right workers with the right employers. The project makes extensive use of elasticsearch, learning to rank, natural language processing, and other machine learning techniques. Before his time at Snagajob, Kowalewski was active in the Washington DC startup scene, creating and working with companies to solve problems using predictive analytics and other advertising technologies.