UrbanSim

From Legacy Constraints to ML-Ready Platform: How We Helped Rebuild a Solution with Predictive Models for Urban Planning

Machine Learning
Urban planning platform
USA
UrbanSim Project

Project highlights

  1. Helped to move from an outdated legacy solution to a modern scalable system
  2. Replaced monolith with flexible microservices architecture
  3. Achieved full DevOps automation of environments
  4. Prepared the infrastructure for MLOps, allowing machine learning adoption
  5. Reduced cloud costs by 40%
  6. Ongoing client engagement
data report

Result

1

Used across the US and internationally

City municipalities in San Francisco, El Paso, and Nashville are already using the new platform, and more clients from the US, Canada, and Australia are about to adopt it soon.

2

Customizable, scalable, ML-ready

The system can be scaled and customized for clients in diverse locations through the use of independent development streams. It's also highly extendable and ready for AI and ML.

3

40% less expensive to maintain

Maintenance and cloud costs have been reduced by almost half through correct DevOps implementation and optimal resource allocation, allowing further scaling with no tech debt.

Related capabilities

Need help moving from a legacy to a new product?

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