Blog

Code Runner: Secure, scalable code execution for model evaluation

Meet Code Runner, the new in-platform code execution engine designed to simplify coding-related tasks and deliver higher-quality datasets for coding-related projects.

Bringing AI to the browser: SAM2 for interactive image segmentation

Learn how to leverage the power of the SAM2 in a web browser, enabling interactive image segmentation without the need for powerful servers or specialized hardware.

Inside the matrix: A look into the math behind AI

Matrices are crucial in AI for processing multi-dimensional data in areas like machine learning and computer vision. They represent linear maps and transform input into output, making them central to many AI methods.

Faster, cleaner code: Labelbox SDK migrates to Pydantic v2

Discover how Labelbox uses Pydantic v2 and the process we followed to migrate from v1 to v2.

Efficient LLM fine-tuning with PEFT

Explore the data and computational requirements of fine-tuning techniques and how you can leverage PEFT to meet them.

Inversion of control and observability for React codebases

Discover how Inversion of Control (IoC) can help React developers build adaptable and scalable apps. Learn to set up IoC using InversifyJS and MobX.

Streamlining large-scale data exports: The evolution of Labelbox’s Export System to a streamable architecture with fairness in place

Learn how the engineering team at Labelbox improved data export efficiency by transitioning from Export V2 to a streamable architecture. By overcoming challenges with advanced queue management, users can now handle large-scale exports more effectively.

The dev corner: streamlining deployments at Labelbox with ArgoCD and GitOps

Learn how we transitioned from a traditional continuous delivery (CD) pipeline to a GitOps approach using ArgoCD.

How to leverage Google’s Gemini models in Labelbox Foundry for building AI

Learn how you can easily evaluate Gemini models, compare them to other powerful foundation models like Open AI’s GPT-4, and choose the best model for your use case with model foundry in Labelbox.

How to confidently compare, test, and evaluate models for machine learning

Learn how to always choose the best and most efficient model for your use case. With Model Foundry, you can test, compare, and evaluate models to confidently select the best performing model.