Your competitive advantage should be data

With over 40 years of experience identifying and investing in innovation, ARK Invest CEO, Cathie Wood discusses what she believes it takes to win in AI with Labelbox CEO Manu Shama.

How the best achieve AI breakthroughs

Labelbox has seen how more than 200 organizations label, manage, and improve their training data. Come learn what sets apart those who move the fastest, realize the largest efficiencies, and find the greatest success in their ML programs.

The need for strong ML capabilities in national security

A panel of experts discusses the responsibilities and challenges the US faces in the pursuit of building preeminent AI capabilities.

Blue River Technologies: Driving efficiencies with model-assisted labeling

Hear what drove Blue River Technologies to adopt model-assisted labeling, enabling them to achieve significant time and cost savings.

NASA JPL case study: Employing ML to find signs of life in our solar system

Mark Wronkiewicz and Jake Lee, both Data Scientists at NASA JPL, will share the story of how they developed an algorithm that helps scientists look for signs of extraterrestrial life. Learn why their ML team decided to invest in a training data platform after using disparate in-house tools for different use cases as well as best practices for managing non-technical labelers.

Lessons learned from my first year with a training data platform

Learn how organizations are successfully moving from a proof of concept to large scale deployment of their ML programs utilizing a training data platform.

Build vs Buy: Exploring the benefits of implementing a home-grown vs. external labeling solution

This panel dives into a question many ML teams consider: should they build their own labeling solutions or invest in a training data platform? Panelists will discuss tactics they used for creating and improving training data before a TDP, the decision to implement a TDP, and what has changed since implementation.

Snowflake

Learn about the Labelbox Connector for Snowflake which enables users to easily annotate unstructured data and warehouse the results for data science use-cases in Snowflake.

DataRobot

Learn how to combine the model environment of DataRobot with the active learning of Labelbox to create and deploy your own Data Engine.

Databricks

Data teams use Databricks and Apache Spark™ to analyze structured data, but may struggle to apply the same analysis to unstructured, unlabeled data. Learn how combining Databricks and Labelbox gives you an end-to-end environment for unstructured data workflows - a query engine built around Delta Lake, fast annotation tools, and a powerful machine learning environment.

Google Cloud Platform

Learn how Labelbox's partnership with GCP enables you to integrate Labelbox with BigQuery and store your assets in GCP.

Introduction to Labelbox

Learn more about the Labelbox platform through a live demo that showcases core functionalities including annotation tools and quality management features.

Expanding our labeling platform: Labelbox updates

Join Group Product Manager David Liang to learn about recent and upcoming updates to our core labeling platform including support for more complex use cases based on video, text, and medical imagery data.

How to optimize your labeling operations set up

We will discuss how Labelbox Workforce solutions can help you achieve both a time and cost savings. Learn more about the different solutions we offer and how to implement them in your current workflow.

Designing the optimal iteration loop

Learn how the latest generation of Labelbox products will redefine how you structure training data pipelines and help you label less data to achieve meaningful performance gains.