mlops-course and MLprod
The two tools are competitors, as both repositories provide course materials and resources aiming to teach the principles and practices of Machine Learning in production environments.
About mlops-course
GokuMohandas/mlops-course
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Covers core ML workloads (data processing, model training, tuning, evaluation) through first-principles lessons that transition from interactive notebooks to production-ready Python scripts with testing and logging. Built on Ray for distributed computing across local laptops, Kubernetes, and cloud platforms (AWS/GCP), enabling seamless scaling without language switching. Integrates MLOps components including experiment tracking, CI/CD pipelines, and model serving while maintaining code parity between development and production environments.
About MLprod
IDSIA/MLprod
Machine Learning in Production
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work