In this workshop, we will learn what it means and how to build an “MLOps workflow” by extending the power of Git and GitHub with open-source tools. In the end, we will have an automated workflow that covers the entire lifecycle of an ML model, from data labeling to monitoring predictions.
Abstract
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.
The workshop only requires a web browser in order to follow from start to finish.
