Guides

How to accelerate and automate data labeling with labeling functions

Learn how teams can accelerate and automate data labeling by using labeling functions with Labelbox.

Distilling a faster and smaller custom LLM using Google Gemini

Learn how to perform knowledge distillation and fine-tuning to efficiently leverage LLMs for NLP, like text classification with Gemini and BERT.

How to enhance RAG chatbot performance by refining a reranking model

Learn how to use Labelbox to preprocess documents (e.g., entity detection, document classification, etc) and improve retrieval relevance by fine-tuning a reranking model.

How to build equipment detection models to improve worker safety and efficiency

Learn how you can leverage Labelbox’s platform to build a powerful task-specific model to improve safety detection for personal protective equipment using the latest advances in foundation models to automate labeling.

What is Human-in-the-Loop?

Every time a new AI tool is rolled out, the topic ultimately shifts to: Will AI replace humans? A satisfying answer to this question is given by the title of a report by Harvard Business Review: “AI Won’t Replace Humans — But Humans With AI Will Replace Humans Without AI”. It summarizes how AI is nothing without the intervention of humans.  In one way or another, humans are involved in developing AI models, integrating natural human intelligence at various points of the machine-learning loop, r

What is multimodal data labeling?

The rise of models like GPT-4o by OpenAI and Gemini by Google have made multimodal models more and more commonplace, and subsequently made multimodal data labeling fundamental to the AI development process.  Multimodal models capture and process different data modalities, becoming the bridge to AI that understands and interacts with the real world. A critical question that developers and consumers of multimodal models should be asking themselves (and aren’t) is: “How do we ensure these multimod

End-to-end workflow with model distillation for computer vision

Learn how to perform model distillation and fine-tuning to efficiently leverage foundation models for computer vision, like object detection with Amazon Rekognition and YOLOv8.

How to do knowledge distillation

Knowledge Distillation, which compresses large, powerful AI models into smaller, faster versions without losing performance, vital for efficient deployment on less powerful devices, has become an important technique for AI development and streamline the process of building intelligent applications.  As advancements in artificial intelligence continue, large language models (LLMs) and deep neural networks (DNNs) are becoming increasingly capable. The latest iterations outperform their predecesso

How to build generative captioning and enrich product listings faster with foundation models

Learn how to leverage Labelbox’s platform and Foundry to build a powerful generative captioning system, ensuring your customers get deeper personalization from LLMs and for your internal teams to derive insights faster from your website product listings.

How to build defect detection models to automate visual quality inspection

Learn how your team can leverage Labelbox’s platform to build a powerful task-specific model to improve defect detection using image segmentation for visual inspection.