Emin Mammadov is a Senior Software Developer specializing in machine learning platforms with 8 years of experience building scalable, production-ready ML infrastructure. Based in Waterloo, he leads MLP efforts at Geotab, architecting Kubernetes-based solutions for experiment workflows, GPU-enabled vector databases, MLflow/Jupyter integration, Ray adoption, and event-driven CI/CD pipelines. His background spans end-to-end MLOps—from Airflow and streaming pipelines in Flink/Spark to asynchronous FastAPI services and custom SDKs for large-scale OLAP access—bridging data science needs with robust engineering. He combines academic research in fault prediction with hands-on platform delivery, and is known for gaining stakeholder buy-in to introduce new orchestration and serving technologies that accelerate model iteration and deployment.
7 years of coding experience
8 years of employment as a software developer
Bachelor's Degree Electrical Engineering, Bachelor's Degree Electrical Engineering at University of Houston
Master of Applied Science Electrical Engineering, Master of Applied Science Electrical Engineering at University of Waterloo
Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.
Contributions:13 commits, 15 PRs, 46 pushes in 1 month
Contributions:8 PRs, 91 pushes, 8 branches in 2 months
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