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Introduction

Part 5 mission patch

Learn how to use the model to label new data using Label Studio and retrain the model iteratively.

Requirements

The following requirements are necessary to follow this part in addition to those described in the first part:

  • A Google Chrome Chrome or a Firefox based browser for better compatibility

State of the MLOps process

Production feedback and new data require a way to improve the model iteratively. In this part, you will address the following issues:

  • Labeling of supplemental data is not systematic or uniform
  • Labeling of supplemental data is time intensive
  • Model needs to be retrained using higher-quality data

Note

This part focuses on labeling data locally so you can experiment without extra infrastructure. For the same reason, we will run dvc repro on your local machine.

In a production setup, you should push the new labeled data to a branch and let your CI/CD pipeline retrain the model on the Kubernetes cluster, as set up in Part 3 - Serve and deploy.