How to Optimize Your Labeling Operations

Building a better AI data engine

AI practitioners regularly face a few common challenges: too much time spent building and maintaining tools and infrastructure, siloed AI development efforts, and fragmented processes to evaluate quality.

Building an effective "data engine" within your organization allows you to improve your model more quickly and reliably. Learn how a training data platform enables you to fuel that engine by harnessing active learning, collaboration, and full transparency into your ML workflow.

Productivity: Workflow set up and ontology defined in just days. Deep experience in project and instruction design to limit mistakes. Trained labeling teams that are ready to go.

Cost control: A team dedicated to driving efficiency and supporting growth while managing cost. Consistent monitoring of workflows and labelers’ performances to minimize risk and maximize quality.

Flexibility: Ability to evolve approach as needs shift or workflows gain efficiency. Combine external teams alongside in-house or existing resources. Weekend and shift work.