Continuum Industries Chooses Iterative to Optimize Civil Infrastructure Design with Evolutionary Computing
Iterative reduced Continuum’s setup and runtime from 48 hours to just three using DVC and CML
SAN FRANCISCO, Jan. 18, 2022 (GLOBE NEWSWIRE) — Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, today announced Continuum Industries, which provides AI tools for engineering professionals to rapidly design linear infrastructure projects, has chosen Iterative-backed open source projects DVC and CML to optimize evolutionary computing optimization workflows and reduce time to market.
Continuum Industries works with large amounts of geospatial data with evolutionary computing algorithms to optimize the design of infrastructure like railways and roads. While Continuum Industries do not use machine learning algorithms, they face a number of the same problems that MLOps aims to resolve. They were looking for a way to have that data sync with the code and be versioned together. After considering a custom build using basic ML tools offered by Amazon Web Services (AWS), Continuum chose Iterative tools for their Optioneer product because they allowed it the freedom to freely integrate various ML tools from other vendors into their workflows (like GitHub Actions for CI/CD in training their models), and begin working on test cases immediately.
“With Iterative, we were able to get started right away without having to maintain it ourselves,” said Ivan Chan, AI engineer at Continuum Industries. “Given the incredible time savings it has already provided, we are planning on expanding our use of DVC to also set up our development and testing environment also to experiment versioning and more.”
With Iterative, Continuum Industries is now able to version everything beyond code, including data, and ML pipelines, and experiments, with DVC, run frequent algorithm tests with reproducible results through Continuous Machine Learning (CML), as well as slash support time. The developer time spent on maintaining Continuum’s suite of algorithm tests has been reduced from five hours every three weeks down to virtually no time at all. Due to the time savings, the team can invest more resources on model development and optimization.
SAN FRANCISCO, Jan. 18, 2022 (GLOBE NEWSWIRE) — Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, today announced Continuum Industries, which provides AI tools for engineering professionals to rapidly design linear infrastructure projects, has chosen Iterative-backed open source projects DVC and CML to optimize evolutionary computing optimization workflows and reduce time to market.
Continuum Industries works with large amounts of geospatial data with evolutionary computing algorithms to optimize the design of infrastructure like railways and roads. While Continuum Industries do not use machine learning algorithms, they face a number of the same problems that MLOps aims to resolve. They were looking for a way to have that data sync with the code and be versioned together. After considering a custom build using basic ML tools offered by Amazon Web Services (AWS), Continuum chose Iterative tools for their Optioneer product because they allowed it the freedom to freely integrate various ML tools from other vendors into their workflows (like GitHub Actions for CI/CD in training their models), and begin working on test cases immediately.
“With Iterative, we were able to get started right away without having to maintain it ourselves,” said Ivan Chan, AI engineer at Continuum Industries. “Given the incredible time savings it has already provided, we are planning on expanding our use of DVC to also set up our development and testing environment also to experiment versioning and more.”
With Iterative, Continuum Industries is now able to version everything beyond code, including data, and ML pipelines, and experiments, with DVC, run frequent algorithm tests with reproducible results through Continuous Machine Learning (CML), as well as slash support time. The developer time spent on maintaining Continuum’s suite of algorithm tests has been reduced from five hours every three weeks down to virtually no time at all. Due to the time savings, the team can invest more resources on model development and optimization.
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