Our client needed a service to structure and form sales territories based on geospatial and demographic data. In a long collaboration we were successful to create a web application that helps the company to serve their customers faster, at higher quality, and with more ease. After all, they can now save about 80 hours of manual work every month.
With the software package, there is no need for our client to keep updating the operating system or migrating to a different environment. They can retrieve the geospatial maps and process it right away without manipulating the data.
The project introduced a central data model to systematically monitor workflows spanning over 7 homegrown and third-party production systems. We reported a potential to decrease time between production and delivery by on average 2-3 days through deploying a regression-based model to calculate estimated production cycle time.
The research paper on 'Challenges and Lessons Learned in CI/CD for ML Applications' delved into the complexities of implementing CI/CD pipelines through real-world case studies. It offered valuable insights and practical recommendations based on the lessons learned from twenty ML projects. The work integrated in-depth insights into ML, software engineering, and cutting-edge technologies, providing in-depth insights into the challenges faced in organization, development, and operations.
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Kommentar
We used to have an outdated tool to map the territories. However, with the rich amount and complexity of data we have, there was a need to invest more time in leveraging new technology. Anyways, the tool was no longer compatible with the latest operating system. So, there was a need to migrate the system and rethink the whole processing.
In result, we realised a much better user experience with a web app interface. Plus, the algorithm performs a lot better than the heuristic approach that we deployed so far.