Using GitHub Actions for MLOps & Data Science
A collection of resources on how to facilitate Machine Learning Ops with GitHub.
github ml-ops production github-actions article code ci-cd tutorial

Machine Learning Operations (or MLOps) enables Data Scientists to work in a more collaborative fashion, by providing testing, lineage, versioning, and historical information in an automated way. Because the landscape of MLOps is nascent, data scientists are often forced to implement these tools from scratch. The closely related discipline of DevOps offers some help, however many DevOps tools are generic and require the implementation of “ML awareness” through custom code. Furthermore, these platforms often require disparate tools that are decoupled from your code leading to poor debugging and reproducibility.

To mitigate these concerns, we have created a series of GitHub Actions that integrate parts of the data science and machine learning workflow with a software development workflow. Furthermore, we provide components and examples that automate common tasks.

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Staff Machine Learning Engineer Currently @github Previously @airbnb, @datarobot
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