mlops-course and mlops-specialization
Both projects are educational resources for learning MLOps, with "GokuMohandas/mlops-course" being a comprehensive course on designing and deploying ML applications, and "mattborghi/mlops-specialization" being notes/resources for a Coursera specialization, making them complementary in the MLOps learning ecosystem.
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 mlops-specialization
mattborghi/mlops-specialization
Machine Learning Engineering for Production (MLOps) Coursera Specialization
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