Labelbox

Mikiko Bazeley

Industry: Any

Product spotlight: Native LLM & multimodal support for hybrid evaluation

Announcement

Seamless LLM human evaluation with LangSmith and Labelbox

Google Cloud partners with Labelbox to offer LLM human evaluation services

Build AI

Labelbox introduces no-code data pipeline integrations

Building computer vision

Labelbox video search powered by Google Gemini Pro Vision

Labelbox Labs - A preview of multimodal data labeling

A pragmatic introduction to model distillation for AI developers

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 leverage Google’s Gemini models in Labelbox foundry for building AI

Use AI

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

Learn how the Texas Rangers Baseball Club used text data to inform player drafting and trading decisions and build a team that secured their first World Series win in November 2023.

How the World Series Champions Texas Rangers built a winning team with AI and analytics

Unleash the full potential of your data in Labelbox with 10x faster performance on large-scale data ingestion uploads. 

10X faster uploads: Labelbox’s data ingestion upgrades and how to use them

Learn how these two methods differ, the pros and cons of each, and how to choose the right method for your fine-tuning project.

RLHF vs RLAIF: Choosing the right approach for fine-tuning your LLM

New in Labelbox: Fine-tune LLMs with our LLM data generation editor, and curate and share data more easily.

Unlock the power of AI: Labelbox’s newest features to use and build better AI

Discover tips and best practices for accelerating your time-to-value when building AI solutions for your retail and e-commerce business. 

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

Industry: Retail & e-commerce

Large language models (LLMs) have revolutionized the field of AI and natural language processing (NLP). Dive into six popular LLMs and explore their intended use cases, limitations, and possible real-world use cases.

6 key LLMs to power your text-based AI applications

Building LLMs

Explore six of the most powerful foundation models available to AI builders, the use cases and applications they are best suited for, and how you can explore, test, and leverage them quickly and easily for pre-labeling data and AI development.

6 cutting-edge foundation models for computer vision and how to use them

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

How to set up your labeling ops for success with observability

MLOps

With foundation models, such as GPT-4, teams now have a way to automate and accelerate data labeling rather than starting from scratch. Leverage Model Foundry's prompt-based interface to create effective prompts for pre-labeling data with GPT-4. 

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

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

In this post, we’ll explore how you can easily leverage a large language model (LLM) to identify and extract PII from your datasets with much higher accuracy, efficiency, and speed.

How to use LLMs to detect and extract personal data from AI datasets

Celebrating Labelbox’s latest security advancement: ISO 27001:2022 Certification

Learn to identify and remove personal identifiable information from data before it puts your AI and organization at risk.

How to detect and extract personal information from datasets for AI

Seamlessly integrate Databricks data pipelines with Labelbox

Explore some of the overarching themes from the sessions, demos, and papers of CVPR 2023.

Key trends and innovations in AI development from CVPR 2023

Recapping CVPR 2023: Key takeaways from Labelbox

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. 

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

Jay Patel

Learn how to accelerate image segmentation with Segment Anything, Auto Segment 2.0 and Model Foundry 

Accelerate image segmentation with a new AI-powered solution

Learn how to accelerate image segmentation with Model Foundry and Labelbox's latest upgrades to the image editor. 

Manu Sharma

Learn how to use LLMs to instantly generate predictions on product categorization and summarization. 

How to automate labeling for product categorization with LLMs

With the introduction of Model Foundry, we will bring the power of foundation models in Labelbox to perform complex tasks such as data labeling and data enrichment. 

Model Foundry: Automating data tasks with foundation models

Labelbox Model Foundry: Automating data tasks with foundation models

Explore best practices for crafting successful prompts specifically for natural language searches.

5 tips to write more effective prompts for natural language search

Explore & manage data

How to write more effective prompts for natural language search

Learn how reinforcement learning from human feedback can be used to train models on tasks that are harder to specify.

Using reinforcement learning from human feedback to fine-tune large language models

Learn more about how you can organize and automate your data labeling with bulk classifications. 

Pre-label and enrich your data with bulk classifications | Labelbeox

Pre-label and enrich your data with bulk classifications

Pre-label and enrich data with bulk classifications

We put GPT-4 and PaLM - two of the largest and most powerful LLMs developed to date - to the test. Read more to learn how GPT-4 and PaLM perform on zero-shot predictive accuracy and generative ability. 

GPT-4 vs PaLM: Assessing the predictive and generative -performance of LLM models

GPT-4 vs PaLM: Assessing the predictive and generative performance of LLM models

Labelbox's upcoming Auto-Segment 2.0 tool will apply the power of Meta AI’s recently released Segment Anything Foundation (SAM) Model.

Auto-segment 2.0 Coming Soon | Labelbox

The upcoming Auto-Segment 2.0 tool will apply the power of Meta AI’s recently released Segment Anything Foundation (SAM) Model in a workflow that helps users rapidly generate high performance training data for their most complex computer vision use cases.

Coming soon: Auto-Segment 2.0 powered by Meta's Segment Anything Model

Label data for AI

Auto-Segment 2.0 powered by Meta's Segment Anything Model

Get a high-level introduction of how vector similarity search works and how it’s helping teams get access to information faster.

How Vector Similarity Search Works | Labelbox

How vector similarity search works

Learn how to utilize the latest advances in zero-shot and few-shot image classifications using OpenAI CLIP embeddings to better curate your data

Utilizing Few-shot and Zero-shot learning with OpenAI embeddings | Labelbox

Train & fine-tune AI

Utilizing Few-shot and Zero-shot learning with OpenAI embeddings

Kahveh Saramout

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 prepare your manufacturing organization for the fifth industrial revolution: AI

Industry: Manufacturing

New in Labelbox: Upload custom embeddings, label video data with step interpolation, send model predictions to your labeling project, and more. 

Upload custom embeddings to find similar data, a new way to label video data, and more.

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).

How to leverage foundation models in manufacturing

Make your videos queryable using foundation models

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

Matt Chambers was recognized for his achievements in render farm management system design with an Academy Award during the Scientific and Technical Awards presentation on February 24th.

Labelbox Engineer Matt Chambers wins 2023 Academy Award

Labelbox Engineer Matt Chambers wins 2023 Academy Award for Scientific and Technical Achievements

Nathana Sharma

As the popularity of LLMs grow in adoption, learn why reinforcement learning (RLHF) is important to help mitigate LLM hallucinations.

What does it mean when an LLM “hallucinates” & why do LLMs hallucinate? | Labelbox

What does it mean when an LLM “hallucinates” & why do LLMs hallucinate?

You can now browse over 30+ large scale public datasets directly through Labelbox. 

Use Labelbox to explore public datasets

You can now browse over 30+ large scale public datasets directly through Labelbox.

New in Model: Prioritize and save high-impact data rows as slices, filter on metadata, and a new way to export data rows across your projects. 

Group, prioritize, and analyze data with slices and a new way to export your data

New in Labelbox Annotate: A variety of improvements to our video editor including a one-click automated bounding box tracking experience, and more. 

Automatically track objects, control playback, and navigate frames on video data

New in Labelbox Catalog: Explore assets from public datasets across use cases, surface data with a natural language search or the "Find text" filter

Explore new public datasets, surface and save data slices

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

Protect your AI: Choosing a secure data labeling partner

Protect your AI: How to choose a secure labeling partner

New in Labelbox: Search for data with natural language querying, navigate Labelbox with less friction, and more. 

Introducing natural language querying, a new navigation experience, and more. 

Introducing natural language querying, a new navigation experience, and more

Discover some of the most impactful recent updates to Labelbox Catalog, Annotate, and Model in 2022.

Year in review: Labelbox 2022 product highlights

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

New in Labelbox Model: Automatically surface model failures and labeling mistakes, debug segmentation models faster than ever, and more. 

Debugging models made easy with auto-computed metrics

New in Labelbox Model: Automatically surface model failures and labeling mistakes, debug segmentation models faster than ever, and more.

New in Labelbox Annotate: Unlock new NER text use cases with the document and conversational text editor, video editor improvements, and more.

Fine-tune language models to your use case with right data

New in Labelbox Annotate: Unlock new NER text use cases with the document and conversational text editor, video editor improvements, and more. 

New in Labelbox Catalog: Preview HTML data rows and the ability to conduct searches with plain text (coming soon)

Better visualize, search, and surface specific data

New in Labelbox Catalog: Preview HTML data rows and the ability to conduct searches with plain text (coming soon) 

Text search and improved data previews

Better enforce feature flag policy with the open-source linter created by Labelbox engineers.

Feature flag linter by Labelbox engineers

Engineering

Learn how leading AI teams set up their model training pipelines with active learning and weak supervision — and how it's boosted performance.

How to improve model performance with active learning and weak supervision

Labeling automation

Learn about some of the most effective automation strategies used by leading AI teams at Nayya and CAPE Analytics.

How to leverage the latest in automation when building AI

How to leverage the latest advances in automation when building AI products

New in Labelbox Model: Teams can soon surface high-impact model failures with auto-generated metrics. 

Find model failures with auto-generated metrics

New in Labelbox Annotate: Optimize model-assisted labeling, queue & review better, and more.

Optimize pre-labeling efficiency with automation metrics

New in Labelbox Catalog for November 2022: One-click similarity search, faster data row identification, and more


Leverage powerful embeddings to surface high-impact data in one click

New in Labelbox Catalog: One-click similarity search, faster data row identification, and more.


Labelbox is launching the automation efficiency score –a new way to track, measure, and improve your pre-labeling efficiency and time savings. 

Measure and improve pre-labeling workflows | Labelbox

Most ML teams operate blindly or struggle to quantify the efficiency of their pre-labeling process. Today, Labelbox is launching the automation efficiency score –a new way to track, measure, and improve your pre-labeling efficiency and time savings in single dashboard. 

Measure and improve pre-labeling workflows with the automation efficiency score

The Labelbox Accelerate 2022 agenda is full of AI builders and leaders sharing practical insights that you can put to use the next day at work.

3 Things you'll learn about at Labelbox Accelerate 2022

Industry: Finance & insurance

3 Things you’ll learn about at Labelbox Accelerate 2022

Teams can identify and fix data row processing errors, easily reorder ontology nodes, and better visualize ground truth and predictions in Model.

Better understand and visualize data rows to surface high-impact data faster

Teams can now identify and fix data row processing errors, easily reorder ontology nodes, better visualize ground truth and predictions in Model, and more. Read on to learn more about the newest features and capabilities on Labelbox.

Learn the three primary reasons enterprises should favor a software-first approach vs. tech-enabled BPOs to handle their data and models.

How a software-first approach to labeling AI data gets you a better ROI than tech-enabled BPOs

Leverage Labelbox for both NER and OCR annotations, view data with our HTML editor, deep zoom on tiled imagery for medical use cases, and more.

Annotate documents, HTML files, high-resolution pathology slides, and queue & review data better with custom workflows

Leverage our document editor for both NER and OCR annotations, view data in a specific manner with our HTML editor, deep zoom on tiled imagery for medical use cases, and more. As we continue to release Workflows across our customer base, teams can more effectively queue and review their data rows. 

We've introduced a new system to identify data rows to allow teams to query faster and prevent duplicate uploads, introduced metadata analytics.

Query and understand your data like never before | Labelbox

We've introduced a new system to identify data rows to allow teams to query faster and prevent duplicate uploads, introduced metadata analytics, and are making a host of exciting improvements to our embedding capabilities. 

Query and understand your data like never before

Prevent duplicate data uploads, save data slices, metadata metrics, and more

Fix label errors for text data, understand model weaknesses with in-depth metrics, and easily compare results with different hyperparameters.

Better visualize and track model errors at every iteration

Your model is only as good as your training data and with these updates, teams can better find and fix label errors for text data, understand model weaknesses with in-depth metrics, and easily compare results for different model runs with different hyperparameters. 

Better visualize and track model errors to improve at every iteration

Labelbox has built improvements to help drive greater insight and productivity across annotation, data curation, and model training workflows. 

Accelerate AI development and search for more granular data with new features

We’ve focused recent product development on a host of improvements to help drive greater insight and productivity across annotation, data curation, and model training workflows. 

Labelbox has been awarded a $950 million ceiling indefinite-delivery/indefinite-quantity contract by the U.S. Air Force. 

Labelbox joins Air Force’s $950 million JADC2 effort

We’ve focused recent product development on end-to-end workflow to help teams iterate and ship models faster than ever.  

Iterate and ship models faster with our newest features

We’ve focused recent product development on end-to-end workflow to help teams iterate and ship models faster than ever. Curate and prepare training datasets, have greater visibility into the processing status and searchability of your data rows, SDK updates, and more. 

This year’s CVPR conference featured over two thousand papers, showing a few  emerging themes and trends in the computer vision field.

3 things we learned at CVPR 2022

3 Things we learned at CVPR 2022

Labelbox is excited to introduce our image auto-segmentation tool to help teams quickly generate and edit quality segmentation masks in seconds. 

Use automation to reduce your labeling time and spend

To train and maintain their models, the Virtusense team designed their data engine to automate nearly every aspect of the iteration loop.

Virtusense builds an AI data engine to increase model performance

Virtusense is the #1 fall prevention system in the world. To train and maintain their models, the team designed their data engine to automate nearly every aspect of the iteration loop.

How Virtusense built an AI data engine that dramatically increased model performance

Industry: Healthcare & life science

How VirtuSense built an AI data engine that dramatically increased model performance

Wading through unstructured data requires a tremendous amount of patience and time. Learn 6 key time-saving practices for AI teams.

6 key best practices for AI teams to save time when creating AI data

Labelbox has focused recent product development to help better optimize the throughput, efficiency, and quality of training data production.

Optimize the quality of your training data with our latest updates

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.

Optimize the efficiency and quality of your training data with our latest updates

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

Learn how Deque developed a sophisticated data engine that discovers model errors quickly and fuels their iterations with high-quality data.

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