Building labeling operations into your core MLOps strategy

We’re seeing that more ML teams are shifting their focus from iterating on models to improving the quality of their training data, labeling operations is becoming a central aspect of their larger MLOps strategy. Creating, managing, and maintaining an efficient labeling pipeline that produces high-quality training data is no easy task. Teams without the right people, processes, and tools in place will likely struggle to generate the training data they need at a cost-effective rate . 

In this guide, you’ll learn about:

• Key labeling operations roles that will benefit every ML team

• Tips to finding the best labeling vendors for your use cases

• Every labeling metric your team should track to ensure quality and efficiency

• How to scale up your labeling operations as your projects change over time