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Making MLOps uncool again

By David


Speaker:: David

Track: PyData: Machine Learning & Stats Machine learning operations (MLOps) have gained attention among practitioners aiming to automate the development of Machine Learning models, attempting to mimic the impact of DevOps in software.

However, MLOps platforms are usually built isolated from the software development process, arguing that the well-proven tools used for DevOps can't be applied to Machine Learning projects.

In this workshop, we will use [HuggingFace]( to train a model that predicts labels for GitHub issues.

By extending the power of Git and Github with [DVC]( and [CML](, our workflow will be able to handle the entire lifecycle of a Machine Learning model using the same tools and platforms that have been proven to work for software development.

Recorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022. More details at the conference page: Twitter: Twitter:


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