In this session Yaron Haviv discusses how to enable continuous delivery of machine learning to production using Git based ML pipelines (Github Actions) with hosted training and model serving environments. Yaron touches upon how to leverage Git to solve rigorous MLOps needs: automating workflows, reviewing models, storing versioned models as artifacts, and running CI/CD for ML. Yaron covers how to enable controlled collaboration across ML teams using Git review processes and how to implement an MLOps solution based on available open-source tools and hosted ML platforms. The session includes a live demo.
This blog has been republished by AIIA. To view the original article, please click: https://www.youtube.com/watch?v=595c8m0SLis
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