Blog

How to set up your labeling ops for success with observability

Explore how labeling quality can be measured and improved with the right observability practices for labeling operations.

Seamlessly integrate Databricks data pipelines with Labelbox

Learn how Labelbox recently upgraded our existing integration with Databricks. We've now made it easier than ever before to connect your Databricks data pipeline with all of your unstructured data for faster AI development with Labelbox.

How to prepare your manufacturing organization for the fifth industrial revolution: AI

To embrace the fifth industrial revolution, manufacturing organizations will need to break down data silos and consolidate their unstructured data into centralized data lakes.

How to leverage foundation models in manufacturing

Think about all the situations in your factory where you need humans to watch a process, identify an event, and then make a decision based on SOPs or other protocols. The bulk of this work can be offloaded to large language models (LLMs).

Labelbox expands partnership with Google Cloud to help teams build AI applications faster

Learn how ML teams can now leverage Labelbox’s AI platform and Google Cloud’s leading AI and Data Cloud tools, solutions, and services, including BigQuery and Vertex AI.

Protect your AI: How to choose a secure labeling partner

Those who build AI must understand the importance of data protection and prioritize it when creating their AI development process.

Year in review: Labelbox 2022 product highlights

Discover some of the most impactful recent updates to Labelbox Catalog, Annotate, and Model.

Optimize the efficiency and quality of your training data with our newest features

We’ve focused recent product development to help users better optimize the throughput, efficiency, and quality of training data production. You can now more powerfully search for data rows within a project, visualize ground truth and predictions in Catalog, and more.

Why data quality is important for machine learning

Why is data quality important in machine learning? Your model is only as good as the quality of its data. Read on to learn more.

The training data practices used by the most successful AI teams

Explore best practices to reduce the amount of time AI teams need to spend on training data.