The guide to labeling automation

Labeling vast quantities of data quickly, efficiently, and accurately is an immense challenge, but advanced machine learning teams have found a way to cut both labeling time and costs with an innovative solution: labeling automation.

Machine learning projects are more likely to succeed when they iterate quickly, as this allows teams to better identify and correct for any biases in datasets and add new datasets as use cases expand and when changes in the real world may affect previous distributions.

Read this guide to learn:

• How to improve training data quality via AI-assisted labeling tools

• Why model-assisted labeling is the labeling automation strategy proven to reduce time and effort

• Ways to overcome the constraints of managing labeling workflows for training models on large datasets via an automated queueing system

• How to create custom workflows to improving your training data quality via simple SDKs and APIs