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The MLOps Course by Goku Mohandas is an open-source curriculum that teaches how to combine machine learning with solid software engineering to build production-grade ML applications. It is structured around the full lifecycle: data pipelines, modeling, experiment tracking, deployment, testing, monitoring, and iteration. The repository itself contains configuration, code examples, and links to accompanying lessons hosted on the Made With ML site, which provide detailed narrative explanations and diagrams. Instead of focusing only on model training, the course emphasizes best practices like modular code design, CI/CD, containerization, reproducibility, and responsible ML (including monitoring and feedback loops). This makes it particularly valuable for engineers transitioning from “notebooks and prototypes” to real systems that must be robust, maintainable, and observable in production.
Features
- End-to-end curriculum covering data, modeling, deployment, monitoring, and iteration for ML systems
- Open-source repository with code, configs, and links to detailed written lessons
- Emphasis on production practices: CI/CD, containerization, testing, and reproducibility
- Teaches modern MLOps patterns such as experiment tracking, model registry, and observability
- Suitable for engineers moving from research prototypes to production ML applications
- Widely recognized and actively referenced in the community as a practical MLOps learning resource
Project Samples
