karan842 / mlops-best-practices

Practical guide to build end-to-end machine learning pipeline and deploy your model in production,

Date Created 2023-04-20 (about a year ago)

Commits 61 (last one about a year ago)

Stargazers 63 (0 this week)

Watchers 2 (0 this week)

Forks 13

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RepositoryStats indexes 598,732 repositories, of these karan842/mlops-best-practices is ranked #403,796 (33rd percentile) for total stargazers, and #487,120 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #10,339/17,660.

karan842/mlops-best-practices is also tagged with popular topics, for these it's ranked: python (#16,642/22396), machine-learning (#6,045/8091), devops (#1,059/1349), azure (#822/1097), data (#719/1002), numpy (#407/545), mlops (#315/422), sklearn (#98/125)

Other Information

Homepage URL: https://medium.com/@karanshingde/machine-learning-in-production-your-comprehensive-101-practical-guide-c7de0b5ad011

All Topics

data azure mlops numpy devops mlflow python sklearn xgboost lightgbm machine-learning

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61 commits on the default branch (master) since jan '22

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updated: 2024-12-11 @ 06:32pm, id: 630403288 / R_kgDOJZMw2A