Project Solution:
The solution for the Footmetics.io project involved utilizing a wide range of technologies, such as React, Python, Django, Flask, JavaScript, Microservices, AWS, Google Cloud Platform, BitBucket, Postman, Swagger, Redis, Celery, GitHub, GitLab, IoT, Data Science, Machine Learning, PostgreSQL, Selenium, Cloud, and CI/CD. My contributions to the project included launching the Footmetics.io website and creating the MVP for the internal product. I also wrote automated jobs to detect camera status, alarms, status alerts, and downtime, along with improving and creating multiple data retrieval APIs. Machine learning algorithms were implemented to forecast sales and remove footfall ghost counting, and dashboards were designed and mapped wireless router footfall with camera footfall on generic cut-off. I also designed and implemented pipelines for file storage and retrieval and converted projects from a monolithic architecture to a microservices-based architecture for performance enhancement. Finally, I performed general maintenance, including bug fixes and performance enhancements for projects. Through my efforts, the product was able to grow from a few customers to having top brands on board in each city of the country, along with footprints in the Middle East and Europe.
The task/challenge for the Footmetics.io project was to create an internal product that could help configure hardware and gather footfall insights from retail brands across the largest cities of the country. Additionally, the project aimed to improve the platform's performance by migrating projects to a microservices-based architecture and designing and implementing pipelines for file storage and retrieval. The project also involved detecting camera status, alarms, status alerts, and downtime, along with the development of machine learning algorithms to forecast sales and remove footfall ghost counting.
Responsibilities:
- Launched the Footmetics.io website and created the MVP for an internal product to help configure hardware and gather footfall insights from retail brands.
- Wrote automated jobs to detect camera status, alarms, status alerts, and downtime.
- Improved and created multiple data retrieval APIs.
- Implemented machine learning algorithms to forecast sales and remove footfall ghost counting.
- Designed and created dashboards and mapped wireless router footfall with camera footfall on generic cut off.
- Converted projects from monolithic architecture to microservices-based architecture for performance enhancement.
- Integrated and designed pipelines for file storage in S3 AWS, DataLake database for data retrieval in front-end.
- Performed general maintenance including bug fixes and performance enhancements for projects.
Achievements:
Grew the product from few customers to having top brands on board in each city of the country, along with footprints in the Middle East and Europe.
This project involved using a wide range of technologies to build and maintain an innovative platform for gathering footfall insights and configuring hardware. My contributions to this project included developing and integrating several key features, such as the automated job system and data retrieval APIs. Additionally, I played a crucial role in enhancing the platform's performance by migrating projects to a microservices-based architecture and designing and implementing pipelines for file storage and retrieval. Through my efforts, the product was able to gain traction and grow significantly in popularity, ultimately attracting some of the most prominent retail brands in the region.
