MLOps Transformation for Global Logistics Project

This document is a case study detailing an MLOps transformation project for a multinational logistics company. The project aimed to implement a robust CI/CD pipeline to automate the deployment of machine learning model updates and develop an automated testing framework to detect regressions and ensure model performance. The client sought to reduce the time-to-market for model updates by 30% within six months, minimize the risk of system failure, ensure scalability for a 50% workload increase, and enforce compliance with regulations such as GDPR. The solution involved setting up Jenkins/GitLab CI/CD pipelines, utilizing cloud-native technologies on AWS/GCP/Azure, and implementing monitoring tools like Prometheus and Grafana. The project also focused on continuous improvement through feedback loops from monitoring data. The expected outcomes included a 30% reduction in time-to-market and enhanced compliance adherence, ultimately driving efficiency and adding value to business operations.