Ramzi Alqrainy,

Paul Anderson photo

The Chefz

Ramzi is Chief Technology Officer at The Chefz - Food Delivery and contributor in Apache Solr and Slack. He is managing more than 40 engineers across 3 regions and building a new version of Food Delivery based on Artificial Intelligence. He is a technical reviewer for a few books related to big data and information retrieval. He has also published papers in IEEE and focusing on search and data functions where he capitalizes on his solid experience in opensource technologies in scaling up infrastructure and information retrieval systems there. Ramzi is passionate about building talented, healthy, and motivated engineering organizations and leading it to accomplish extraordinary things. He cares deeply about organizational health and principled leadership, as these are the greatest drivers for any team to harness its maximum potential. He was building OpenSooq as a CTO from scratch to be the biggest classified application in the Middle East and North Africa to serve more than 45 Million Users and 2.5 Billion Pages views Monthly. Solid experience in Solr, ElasticSearch, Spark, Mahout, and Hadoop stack contributed directly to the business growth through the implementations and projects that helped the key people at OpenSooq slice and dice information easily throughout the dashboards and data visualization solutions. He holds a Master's degree in Artificial Intelligence, and was among the first rank in his class, and was listed on the honor roll. With more than eight full stack search engine development, he was able to solve many complicated challenges about agglutination and stemming in the Arabic language. He holds a Master's degree in Computer Science, and was among the first rank in his class and was listed on the honor roll. Building a classified application from scratch to serve more than 2 Billion page views monthly with a serverless multitier architecture combined with service-oriented architecture. (Microservices)

Ramzi Alqrainy, is speaking at the following session/s

Non-English Search as Machine Learning Problem For Food Delivery

Search relevance is how questions are answered through search. It's the process of changing the ranking of search results for a user query to return what users want. A search for 'iPhone XS' should rank documents highly when the product name matches. But a different query, 'smartphone with two cameras' would require a completely different strategy for ranking candidate results. What gives teams a headache is that all the diverse use cases for search must be handled by a single ranking algorithm.

Attendee Takeaway

In this session you will learn how search can be treated as a machine learning problem and how 'Learning to Rank' takes the step to returning optimized results to users based on patterns in usage behavior. Ramzi will talk through where Learning to Rank has shined, as well as the limitations of a machine learning based solution to improve search relevance.