High-Performance Entity Matching Solution - Rishabh Bhardwaj | Vector Space Talks #005

High-Performance Entity Matching Solution with Qdrant by Rishabh Bhardwaj, Data Engineer at HRS Group

Abstract:
This talk presents a high-performance solution for hotel matching using AWS infrastructure, Qdrant, and vector embeddings. The problem of matching hotel information from various sources is discussed, highlighting its importance for the travel industry. The solution leverages vector embeddings for precise matching and integrates seamlessly with AWS services for scalability and cost-effectiveness. Key components include MiniLM-L6-v2 for embeddings, serverless inference with AWS Sagemaker, efficient vector storage using Qdrant, and Lambda with API Gateway for serving matching requests. The talk shares insights gained through experimentation and showcases how this solution significantly improves speed and accuracy in hotel mapping, ultimately enhancing customer satisfaction and operational efficiency in HRS.

Rishabh's Bio:
As a data engineer at HRS group, I'm responsible for designing, developing, and maintaining data pipelines and infrastructure to support the company's data-driven decision-making processes. With several years of experience in the field, I bring a deep understanding of data engineering principles and best practices to my work.

My expertise spans a range of technologies, including SQL, Python, Airflow, ETL tools, and cloud platforms such as AWS and Azure. I'm skilled in data modeling and data integration, and I have a proven track record of delivering high-quality data solutions that meet business needs.

At HRS group, I work closely with data analysts, scientists, and other stakeholders to understand their requirements and provide them with the data and insights they need to make informed decisions.
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https://www.linkedin.com/in/dev-rishabh/

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