Origins-AI & Amazon: Efficiency & Optimization
The collaboration between Origins-AI and Amazon focused on enhancing efficiency and cost-effectiveness within the Amazon Sellers Promotion & Deals Team. As a Software Engineer and DevOps specialist, Origins-AI contributed to system design, project leadership, and cost optimization, helping streamline operations for deal management. Key accomplishments included designing a bulk editing system, reducing AWS costs through data pattern analysis, and migrating legacy systems, ultimately enabling Amazon to offer more competitive promotions and improve overall platform functionality.
About Amazon
Amazon India, a subsidiary of Amazon.com, launched in 2013, has become a leading e-commerce platform in India, offering a vast array of products across categories such as electronics, fashion, home essentials, and more. The platform provides services like Amazon Prime, which includes benefits such as free and fast delivery, access to Prime Video, and exclusive deals. Amazon India also supports local businesses through initiatives like Amazon Saheli and Amazon Karigar, promoting products from women entrepreneurs and artisans. Additionally, the company has invested significantly in infrastructure, including fulfillment centers and data centers, to enhance customer experience and support its operations in the country.
Our Partnership with Amazon
Industry
Technology & Electronics
Services
IoT Data Analysis and Model Development
Business Type
Research & Development
Technologies Used
Python
Machine Learning
Server Development
IoT Sensors
Data Visualization Tools
The Challenges
BES (Bulk Editing System)
Designing an efficient bulk editing system for deals and promotions was challenging due to the complex data structures and varying requirements of Amazon’s promotional tools. Ensuring compatibility across multiple product categories and promotion types required extensive planning and testing.
AWS Cost Management
High AWS costs were a persistent issue, especially with fluctuating data access patterns that impacted storage and retrieval expenses. Identifying and addressing inefficiencies in data handling was crucial to control costs without affecting performance.
Ticket Resolution Backlog
Resolving a backlog of operational and programming tickets presented a challenge in maintaining system stability and handling diverse issues quickly. Many tickets required cross-functional knowledge and fast debugging, often with limited documentation on legacy systems.
Legacy Codebase Migration
Migrating dependencies of an extensive legacy codebase to the latest libraries and frameworks posed compatibility challenges and the risk of service disruptions. This required careful testing to ensure new libraries would integrate seamlessly with the existing architecture.
The Solutions
Streamlined Bulk Editing System
We developed a system that allowed users to perform bulk edits on deals and promotions across various categories.
AWS Cost Optimization through Data Analysis
By analyzing data access patterns, we identified high-cost areas and restructured data storage and retrieval processes. This solution not only lowered AWS costs significantly but also improved data access speeds, benefiting overall system performance.
Ticket Management and Quick Resolutions
Implemented a structured ticket prioritization process to handle the backlog efficiently, addressing high-impact issues first. Streamlined deployment processes helped resolve 87 tickets within a span of 84 hours, reducing the strain on system operations and enhancing user satisfaction.
Legacy Code Modernization
To migrate the legacy codebase, we used automated testing and gradual integration strategies. Each dependency was updated and tested incrementally, which minimized risks and ensured a smooth transition to updated libraries.
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