Guides

Automatically label images with 99% accuracy using foundation models

Automatically label images with 99% accuracy leveraging Labelbox's search capabilities, bulk classification, and foundation models.

How to kickstart and scale your data labeling efforts

Learn how to effectively kickstart and scale your data labeling efforts to reduce cost, while maintaining the desired quality required for your use case.

How to evaluate and optimize your data labeling project's results

Learn how to evaluate the results of your labeling project in order to further optimize and improve future iterations and batches of data.

How to create a quality strategy for your data labeling project

Learn how to create a quality strategy to ensure your project is producing valuable, high-quality training data for your use case.

How to define your data labeling project's success criteria

Learn how to effectively define your labeling project's success criteria so all tasks lead to consistent output with high quality.

How to define a task for your data labeling project

Learn how to align on key components of your project: define a task, create an ontology, and determine timelines for your labeling project.

How to train a chatbot

The rise in natural language processing (NLP) language models have given machine learning (ML) teams the opportunity to build custom, tailored experiences for their customers. Learn how to train a domain-specific chatbot.

How to scale up your labeling operations while maintaining quality

Many ML teams are eager to label all their data at once. However, this can actually increase time and cost. Learn how you can effectively build an iterative approach to your labeling operations to ensure quality while scaling.

How to maintain quality and cost with advanced analytics

Delays from quality management or the lack of insight into labeling quality can hinder model development. Learn how to maintain quality and cost with project performance dashboard and advanced analytics for Enterprise teams.

How to customize your annotation review process

Custom workflows can help optimize how labeled data gets reviewed across multiple tasks and reviewers. Workflows is a new feature that allows teams the flexibility to tailor their review workflows for faster iteration cycles.