TL;DR
Modernized ML CI/CD pipelines by moving to declarative Jenkins and automating retraining and deployment. Cut deployment time significantly and gave ML team full ownership, unlocking faster iteration and delivery.
The machine learning team was operating with fragmented, scripted Jenkins pipelines that were difficult to maintain and iterate on. This bottleneck made it slow to retrain, test, and deploy models, limiting the team’s ability to respond quickly to new data or changing requirements.
I set out to modernize and streamline the team’s CI/CD process as part of a broader push toward continuous delivery. The objective was to reduce deployment time, improve maintainability, and give the ML team more direct control over their own delivery workflows.
I refactored the Jenkins setup by migrating from a scripted to a declarative pipeline architecture. This included relocating instructions into version-controlled Jenkinsfiles directly within the repositories, ensuring transparency, modularity, and reproducibility. I worked closely with the DevOps team to ensure a smooth handover of pipeline ownership to the ML engineers. Alongside the infrastructure changes, I also automated the retraining and evaluation processes for machine learning models, enabling automated deployment to production whenever models passed testing thresholds.
The transition led to a significant reduction in deployment time and allowed the machine learning team to iterate and ship models much faster. By automating retraining and enabling CI-driven deployment, we moved from slow, manual updates to a robust continuous delivery setup—boosting development velocity and responsiveness across the board.