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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.
Introduced a team of Data Scientist to Test Driven Development, GitLab CI/CD, and how to apply the tools to the specific use for a data and ML pipeline.
We realised to educate the team in modern software engineering methods boosted their performances by miles.
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 organisation, development, and operations.
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Automated testing and a streamlined CI/CD pipeline was long over due for our product. Aiku ran a series of sessions and tutorials with our team to introduce us to TDD and GitLab.