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.

90h + 45h 2 certificates available

Machine Learning SystemsMLOpsDistributed TrainingFeature EngineeringModel Serving

Curriculum

2 complete tracks. Study any or all — 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 neu

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

How you study

Every lesson pairs an in-depth video with written notes, key takeaways, a hands-on lab, exercises and a quiz. Work at your own pace, then sit the final examination for your Erudex certificate.

Machine Learning Engineering

Curriculum

Module 1Foundations of Distributed Training and Algorithmic Parallelism

Module 2Inference Compilation, Graph Optimization, and Numerical Precision

Module 3Statistical Validation and Distributional Shift Formalisms

Module 4Data Contracts, Temporal Semantics, and Feature Infrastructure

Module 5Verifiable Pipelines, Provenance, and Reproducible Execution

Module 6Software Architecture and Continuous Delivery for ML

Module 7Reliable Model Serving and Capacity Engineering

Module 8Operational Observability and Safe Adaptive Systems

Module 9Security, Privacy, and Accountable ML Governance

Module 10Capstone: An Auditable Production ML Platform

Careers this prepares you for

How you study