Covering everything you need to know in order to build AI products faster.
Labelbox•June 24, 2022
6 key best practices for AI teams to save time when creating AI data
Wading through a vast amount of unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. Learn six key time-saving practices for ML teams to implement when handling AI data.
Labelbox•February 23, 2022
How to create high-quality training data
Improving the quality of training data can mean something different for every use case, model, and even iteration cycle.
Labelbox•January 20, 2022
How to improve model performance with less data
This data-centric ML method can reduce the required amount of training data by 10% to 50%.
Labelbox•November 10, 2021
Stop labeling data blindly
ML teams need to know "what data will improve model performance fastest?" These best practices will help teams identify model errors and prioritize better data.
Labelbox•October 29, 2021
You’re probably doing data discovery wrong
Learn best practices for storing, evaluating, and visualizing labeled and unlabeled data so you can accelerate your model iteration cycles and simplify your training data pipeline.
Labelbox•October 5, 2021
How to build trust in your machine learning models
Getting buy-in from stakeholders can be a tough challenge for ML teams who want to build transformative, enterprise models that support major operations.
Labelbox•September 29, 2021
Labelbox lands in London: The key to a performant ML model lies within your training data
In a data-centric approach to machine learning, no element is more essential in your ML endeavors than creating and maintaining high-quality training data. Many ML teams fail due to two common but critical mistakes.
Labelbox•August 9, 2021
Three bite-sized videos to help you get started with Labelbox
Unlike many service providers and in-house labeling tools, you can start using Labelbox to create training data in a matter of minutes.
Labelbox•February 2, 2021
The case for training data platforms
In a new VentureBeat article, AWS Principal Matt Asay delivers a compelling argument for enterprises adopting AI projects.
Labelbox•January 26, 2021
4 Things we learned from the state of training data
We held a short poll to gauge how ML teams are handling the training data creation process. Here are four compelling takeaways from the poll.
