Covering everything you need to know in order to build AI products faster.
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.
How to search, surface, and prioritize data within a project
The Data Rows tab is the central hub for all data rows within a given project. You can view, manage, and filter for data rows within your project to better prioritize data for labeling and to accelerate model development.
How to prepare and submit a batch for labeling
High-quality training data is crucial to the success of any ML project. Rather than queueing an entire dataset for labeling, queuing Data Rows with batches gives teams greater control and flexibility in the prioritization of a project’s labeling queue.
A new way to queue & review
A migration guide for the switch to Batch-based queueing, Workflows, and the Data Rows tab.
How to natively annotate a PDF document
Easily turn stores of documents and PDF files into performant ML models with our Document editor. With the ability to use an NER text layer alongside OCR techniques, teams can annotate text, images, graphs, and more without losing context.
