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Beyond benchmarks 

We are going beyond traditional benchmarks to measure the likability and performance of leading AI models. Labelbox leaderboards measure model capabilities by using its data factory: platform, scientific process and expert humans.

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Programmatically launch jobs

Labelbox's Python SDK simplifies the orchestration of human data labeling projects, enabling programmatic control over every step, from data import to export. This empowers AI teams to efficiently manage labeling jobs, incorporating high-quality labeled data into their workflows and accelerating their AI development process.

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Building the data factory

Labelbox has built the modern AI data factory by expanding its Labeling Services and enhancing its core platform to accelerate data delivery, generate high-quality data, and improve control over labeling tasks. The new multimodal editor, offline multimodal conversations, and other features streamline the creation and evaluation of complex AI models, especially for Generative AI and RLHF applications.

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Model Foundry brings the power of foundation models into Labelbox to perform complex tasks such as data labeling and data enrichment. Early results indicate that over 88% of labeling tasks can be meaningfully accelerated by one or more foundation models. You will be able to access the world's best AI models from all major providers and open source alternatives. These models will act as a copilot for subject matter experts to accelerate model development.

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Achieving breakthroughs in baseball data with analytics and AI

Zero-Shot Learning vs. Few-Shot Learning vs. Fine-Tuning: A technical walkthrough using OpenAI's APIs & models_Guide_Header

Zero-Shot Learning vs. Few-Shot Learning vs. Fine-Tuning: A technical walkthrough using OpenAI's APIs & models

How to evaluate object detection models with Model Foundry_Guide_Header

How to evaluate object detection models with Model Foundry

How to analyze customer reviews and improve customer care guide header

How to analyze customer reviews and improve customer care with NLP

In this guide, we’ll walk through how your team can leverage Labelbox’s platform to build an NLP model to improve customer care. Specifically, this guide will walk through how you can explore and better understand review topics and classify review sentiment to make more data-driven business decisions around customer care initiatives. 

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How to accelerate time-to-value when building AI for retail and e-commerce

How to use Labelbox and Google Cloud to deliver successful AI systems for improving merchandising, enhancing customer care, optimizing marketing strategies in significantly less time.

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How to unlock the full power of GPT-4 for enterprise AI

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Labelbox Model Foundry provides access to a range of popular models, including third-party APIs and open source models. Categorized by their intended uses, you can quickly browse and select a model that is the best fit for your desired use case. After selecting a model, such as GPT-4, you can use our prompt-based interface to easily create effective prompts for generating accurate pre-labels.

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Less than a decade ago, generating entire software programs, marketing campaigns, and works of art in a matter of minutes or hours would have been impossible. Today's workforce, however, is armed with AI solutions like Copilot, ChatGPT, Stable Diffusion and more — with new and more powerful tools launching every day. AI has become an integral part of our day-to-day lives.


For enterprises, it's more important than ever to use AI effectively and quickly to build and maintain their competitive advantages. Today's AI teams have more computing power, more data, and more pre-trained models than before, but they also face more challenges and setbacks. An AI platform offers the tools and infrastructure to help mitigate these challenges and accelerate AI development. Read the guide to learn how.

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Discover a smarter, easier approach to building AI products faster.

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Learn which enterprises are leading the way by innovating faster with AI.

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Discover how leading AI teams do training data and see real-life use cases.


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The scientific approach to generating the highest-quality data 

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Multimodal support for GenAI 

Labelbox's new multimodal chat solution empowers GenAI developers by providing advanced tools for creating, evaluating, and optimizing model responses across various data modalities. It streamlines the data labeling process and offers access to subject matter experts, enabling teams to improve AI models with high-quality, human-evaluated data.

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Evaluating text-to-image models

This blog post presents a comprehensive evaluation of three text-to-image models (DALL-E 3, Stable Diffusion, and Imagen 2) using both human preference ratings and automated evaluation techniques. The results reveal that while Stable Diffusion generally ranked highest in human evaluations, DALL-E excelled in prompt adherence, and Imagen 2 produced the highest technical image quality, highlighting the complex relationship between quantitative performance and human perception of quality.

Building labeling operations into your core MLOps strategy

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The people, processes, and tools your ML team needs to create high-quality training data

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Learn how an AI platform accelerates AI development by offering the tools and infrastructure for AI teams to explore data, label data, train models, and more.

The guide to unlocking AI with your data

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Labelbox - The complete guide to data engines for AI

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Learn how an AI data engine is the foundational infrastructure for how team members interface with data and models in order to build better AI products — and do it remarkably fast.

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The complete guide to data engines for AI

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Discover the four common roadblocks faced by AI teams across industries and use cases and 10 processes and strategies to help overcome them.

10 training data practices used by the most successful AI teams

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The decision to build your own data labeling infrastructure depends on multiple different factors, including model complexity, annotation volume, and opportunity costs. Read our guide to see if you should build it yourself — or buy one.

Should I build or buy an AI data engine?

The Labelbox Guide to Labeling Automation

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Download The Labelbox Guide to Labeling Automation to learn more about how advanced labeling teams cut labeling time and costs with a robust labeling automation strategy.

Download The Labelbox Guide to Labeling Automation

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How designing a workflow involving automation and iteration can lead to more accurate models, faster implementation and 70% cost savings

Building a better AI data engine

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The definitive guide to buying a training data platform

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We've created a comprehensive checklist of features and capabilities that a TDP should have to best serve your specific requirements as your ML needs scale.

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Why Insurance AI requires a training data platform

Why insurance AI requires a training data platform

Image Annotation Guide

Download The Complete Guide to Image Annotation

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The complete guide to image annotation

Complete guide to image segmentation

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Download the complete guide to image segmentation from Labelbox

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The complete guide to image segmentation

Ebook Python SDK

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A Guide to the Labelbox Python SDK

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A guide to the Labelbox Python SDK

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Webcast: How to build AI Applications on Labelbox

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How to build world-class AI Applications with Labelbox

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Discover how Labelbox helps data science teams use model assisted labeling to label data faster and reduce labeling costs.

Leveraging model-assisted labeling to better harness AI data

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Model-assisted labeling webcast

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Watch the presentation given by Labelbox COO, Brian Rieger at ECCV 2020.

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ECCV presentation by Brian Rieger

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A conversation between Gareth Jones (Machine Learning Engineer at Arturo) and Brian Rieger (Co-founder & COO at Labelbox).

Webcast: Building sophisticated AI-first products from the ground up

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Building sophisticated AI-first products from the ground up

Five key considerations for scaling your Labeling Operations

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Discover the five things you need to know before scaling data labeling

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Five key considerations for scaling your data labeling operations

Labelbox security overview

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A guide to security, data privacy and compliance with Labelbox

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Watch our on-demand webcast from lead ML engineer, Randall Lin on how to best size your data requirements for your machine learning model.

How much training data do you really need?

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Watch our on-demand webcast on how Labelbox accelerates high-performing AI into production as a training data platform

Accelerating AI on Labelbox

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Join Labelbox co-founder Brian Rieger and Sr. Product Manager David Liang for a discussion about how to measure throughput, efficiency, and quality to help you optimize labeling operations.

Get the most out of your labeling operations

Wading through vast amounts of unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. Drawing from the experiences of hundreds of AI teams across industries, we are sharing six time-saving practices for ML teams to implement when handling AI data. In this guide, you’ll discover how to:
Annotate faster with a dynamic queueing system 
Improve communication, collaboration, and consensus between teams
Utilize a programmatic-approach for quicker access to data 
Leverage software optimized for speed
Incorporate automation through model-assisted labeling
Utilize active learning and prioritize the right data

These strategies will unblock key barriers and speed up overall processes and capabilities for a quicker path to production AI.

The guide to saving time when creating AI data

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Six key time-saving practices for ML teams to implement when handling AI data

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Training data quality

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Learn how ML teams can dramatically improve the quality of their labeled data and improve models in order to build better AI products.

How to upgrade your training data quality: Proven strategies used by leading AI teams

How to upgrade your training data quality

Guide to labeling automation

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Learn how leading AI teams are cutting both labeling time and costs with an innovative solution: labeling automation.

The definitive guide to labeling automation

The guide to labeling automation

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Practical tips for media and entertainment companies

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Learn more about our approach to human-centric AI evaluation

Programmatically launch human data jobs

Building the data factory for GenAI and frontier models

Automate data tasks with foundation models

Achieving breakthroughs in baseball data with analytics and AI

Zero-Shot Learning vs. Few-Shot Learning vs. Fine-Tuning: A technical walkthrough using OpenAI's APIs & models

How to evaluate object detection models with Model Foundry

How to analyze customer reviews and improve customer care with NLP

How to accelerate time-to-value when building AI for retail and e-commerce

Try Labelbox today