mlrun and MLOps

MLRun is a comprehensive, actively-maintained production platform with extensive integration capabilities, while the MLOps repository appears to be an educational or reference resource, making them complementary rather than competitive—one could use MLRun as the orchestration backbone while consulting the other for MLOps best practices documentation.

mlrun
82
Verified
MLOps
61
Established
Maintenance 22/25
Adoption 11/25
Maturity 25/25
Community 24/25
Maintenance 10/25
Adoption 10/25
Maturity 16/25
Community 25/25
Stars: 1,656
Forks: 296
Downloads:
Commits (30d): 77
Language: Python
License: Apache-2.0
Stars: 278
Forks: 394
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: CC0-1.0
No risk flags
No Package No Dependents

About mlrun

mlrun/mlrun

MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.

This platform helps data scientists and ML engineers quickly build and manage AI applications throughout their lifecycle. It takes your raw data and machine learning code, then automates the processes for data preparation, model training, deployment, and ongoing monitoring. The output is a continuously running, production-ready AI application or service.

MLOps Generative AI development Machine Learning Lifecycle Data science production AI application deployment

About MLOps

raminmohammadi/MLOps

Machine Learning In Production (MLOps)

This project provides practical learning materials for setting up and managing machine learning systems. It helps you take raw machine learning models and turn them into reliable, scalable applications that can be used by real people. You'll get step-by-step guides and code examples, and in return, you'll gain the skills to build robust ML pipelines and oversee their performance in live environments. This is for data scientists, machine learning engineers, and IT professionals who want to bridge the gap between model development and production.

machine-learning-engineering model-deployment production-ML LLM-operations data-science-workflow

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