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USA
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Client Background
The client is an AI technology firm enabling large enterprises to integrate diverse data sources and build faster, more precise machine learning models.
Client Need
The client faced challenges in integrating their internal organizational datasets with the vast array of publicly hosted datasets and experienced performance bottlenecks in joining their internal and external datasets, resulting in slow query times and poor user experience.
Solution
We implemented a comprehensive solution to address these challenges:
User-Friendly Data Science Platform: Implemented a responsive and modern frontend application using Redux React, enhancing the user experience for data scientists
Scalable Middle-Tier Infrastructure: Designed and implemented all middle-tier services, including APIs and a data access layer, on Python Django
Automated Query Generation: Developed an ANSI SQL code generator in Python that intelligently interprets user selections, interacts with the metadata system, and generates optimized queries for Snowflake
Intelligent Search and Recommendations: Built search and recommendation systems on Neo4j, enabling users to quickly find features relevant to their uploaded datasets
Realized Benefits
The solution yielded significant benefits:
Enabled complex joins, including various mathematical functions, between large datasets within seconds, delivering outputs of billions of rows using Snowflake
Automated the creation of multiple clusters based on concurrent query counts, ensuring optimal performance as workload increases
Accelerated data scientists’ model iteration, leading to higher accuracy levels, by significantly reducing the time spent on finding relevant features
Tools & Technologies
Numpy
Django
Redux
React
AWS
Snowflake
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