Machine Learning Engineering
Architect, deploy, and monitor scalable machine learning systems with mathematical rigor and production engineering.
Master the transition from exploratory ML prototypes to resilient, enterprise-grade production software. This course bridges statistical learning foundations with distributed computing, CI/CD orchestration, feature stores, and automated model monitoring. Students systematically learn to optimize inference latency, scale distributed training, and enforce pipeline governance.
Curriculum
Two complete tracks. Study either or both — each has its own exam and certificate.
A mathematically rigorous treatment of distributed machine learning systems, compilation graph theory, optimization dynamics, and formal statistical monitoring foundations.
Examines the theoretical boundaries and communication complexity of parallelizing deep neural network optimization across clustered hardware nodes.
- Mathematical Formulations of Data vs. Model Parallelism Lab35 min
- Ring-AllReduce and Collective Communication Primitives Lab40 min
- Stochastic Optimization under Asynchronous Communication Lab45 min
Careers this prepares you for
- Machine Learning Engineer
- MLOps Infrastructure Engineer
- AI Platform Engineer
- Production ML Specialist