mlops-specialization and coursera-mlops-specialization

These two tools are competitors, as both appear to be separate, user-created repositories mirroring or supplementing content for the same Coursera MLOps Specialization course.

Maintenance 0/25
Adoption 8/25
Maturity 16/25
Community 20/25
Maintenance 0/25
Adoption 7/25
Maturity 16/25
Community 19/25
Stars: 47
Forks: 30
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 26
Forks: 22
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About mlops-specialization

mattborghi/mlops-specialization

Machine Learning Engineering for Production (MLOps) Coursera Specialization

These notes summarize the Machine Learning Engineering for Production (MLOps) Coursera Specialization. They condense key concepts and workflows for building and managing machine learning systems in real-world environments. Data scientists, machine learning engineers, and MLOps practitioners who are learning or solidifying their understanding of production-grade ML systems will find these notes useful.

Machine Learning Operations ML Engineering Production ML Data Science Training ML System Design

About coursera-mlops-specialization

johnmoses/coursera-mlops-specialization

Coursera Machine Learning Engineering for Production Specialization Course

This specialization provides a comprehensive curriculum for machine learning practitioners looking to transition their theoretical knowledge into practical, real-world applications. It guides you through the entire lifecycle of a machine learning project, from initial data collection and model training to deployment and ongoing management. This is for anyone who wants to take their machine learning models from an experimental stage to a reliable, operational system.

machine-learning-engineering MLOps data-pipeline-management model-deployment production-AI

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