ML Ops and Cloud Engineering
Design scalable ML systems with AWS/GCP, Docker, Kubernetes, CI/CD pipelines, and MLflow.
Course overview
Design scalable ML systems with AWS/GCP, Docker, Kubernetes, CI/CD pipelines, and MLflow.
What you’ll learn
- Design scalable and production-ready machine learning architectures on cloud platforms
- Containerize machine learning applications and services using Docker
- Orchestrate containerized deployments efficiently with Kubernetes
- Build automated CI/CD pipelines for continuous model training and deployment
- Track experiments, manage model artifacts, and use registries with MLflow and DVC
- Monitor model performance, latency, and data drift in production environments
Course Curriculum
A structured, progressive curriculum designed to build depth, intuition, and real-world proficiency over time.
This section introduces key concepts and builds intuition through structured lessons and exercises.
Who this course is for
- Basic understanding of Python programming and machine learning fundamentals
- Familiarity with command-line interface (CLI) and basic Linux commands
- No prior cloud or DevOps experience required
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