Machine Learning Engineering Program
- 9-month specialized program for engineers who want to build not just models, but production-ready ML systems that scale to millions of users
- Master the entire ML engineering stack: data pipelines, model training, serving infrastructure, monitoring, and automated retraining workflows
- Collaborate with senior ML engineers from Meta, Netflix, and Uber on real capstone projects deployed to production cloud environments
Program Duration
Learning Mode
Weekly Commitment
Certificate
Program Highlights
Key features that make this program stand out
Production ML Systems
Design and implement ML systems that handle real-time inference at scale using TensorFlow Serving, Triton, and Ray
Feature Stores & Data Pipelines
Build robust data pipelines with Apache Kafka, Airflow, and Feast feature store for consistent ML feature serving
MLOps Certification
Earn a professional MLOps certification and prepare for Google Professional ML Engineer and AWS ML Specialty exams
Model Monitoring
Implement drift detection, model performance monitoring, and automated retraining systems using Evidently and Prometheus
ML Pipelines
Build automated ML training and deployment pipelines with Kubeflow, MLflow, and GitHub Actions
Engineering Mentors
Weekly 1-on-1 mentorship sessions with senior ML engineers who've built recommendation systems, fraud detection, and NLP at scale
About This Course
The Machine Learning Engineering Program bridges the gap between data science research and production software engineering. Many data scientists can build models — far fewer can build systems that reliably serve those models at scale, monitor them in production, and retrain them automatically when performance degrades.
This program is built around that gap. You'll master the engineering disciplines that turn experimental notebooks into robust, scalable ML systems: distributed training, model serving, feature stores, data validation, CI/CD for ML, and observability.
By the end of this program, you'll be able to design and implement the full ML system stack, from data ingestion to model serving, monitoring, and automated retraining. You'll have deployed multiple production systems and earned the credentials that top ML engineering teams look for.
“The difference between a good data scientist and a great ML engineer is the ability to build systems that are reliable, scalable, and maintainable. This program teaches you exactly that.”
— Dr. Lisa Thompson, Program Director & Former ML Lead at Netflix
Ready to Start?
Join 980+ students already enrolled in Machine Learning Engineering Program. The next cohort starts soon.