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We all love the conventional uses of CI/CD platforms, from automating unit tests to multi-cloud service deployment. But most CI/CD tools are abstract code execution engines, meaning that we can also leverage them to do non-deployment-related tasks. In this session, we’ll explore how GitHub Actions can be used to train a machine learning model, then run predictions in response to file commits, enabling an untrained end-user to predict the value of their home by simply editing a text file. As a bonus, we’ll leverage Apple’s CoreML framework, which normally only runs in an OSX or iOS environment, without ever requiring the developer to lay their hands on an Apple device. And we’ll see how self-hosted runners can be used to take advantage of GPUs on custom architectures for Deep Learning.
- CI/CD platforms can be leveraged for abstract execution such as ML
- This reduces or eliminates the need for other services/hardware
- Deep Learning and custom execution environments can be easily accessed/attached
- Predictions can be executed via low-barrier triggers, such as a git commit