MLOps¶
Description |
“ML Operations” is the combination of “DevOps” and Machine Learning. Tools in the MLOps space provide the infrastructure to put machine learning models in production. These tools may include training and testing of models, a generic pipeline/workflow mechanism, model registries, scalable model serving via HTTP, and metrics capture. |
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Projects |
5 |
Lines Committed vs. Age Chart (click to view) |
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Projects¶
Project |
Size Score |
Trend Score |
Byline |
|---|---|---|---|
7.5 |
4.0 |
The Machine Learning Toolkit for Kubernetes |
|
4.25 |
5.75 |
Build and manage real-life data science projects with ease |
|
9.25 |
8.5 |
An open source platform for the machine learning lifecycle |
|
7.0 |
3.5 |
Machine Learning Platform for Kubernetes (MLOps tools for experimentation and automation) |
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5.75 |
7.0 |
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models |