San Francisco, California
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About Us

Data curation, AI-assisted labeling, model training & diagnostics, labeling services, and intuitive APIs, all in one platform, to build better AI products, remarkably fast.

Our mission is to build the best products for humans to advance artificial intelligence.

We believe that AI has the power to transform every aspect of our lives — from healthcare to agriculture. The exponential impact of artificial intelligence will mean mammograms can happen quickly and cheaply, irrespective of the limited number of radiologists there are in the world, and growers will know the instant that disease hits their farm without even being there.

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Training successful chatbots requires the highest quality training data. Learn how @Walmart's data science teams leverage Labelbox, a critical piece of their data engine, to label the data that powers their Text to Shop capabilities.


Using your model's output as pre-labels can significantly accelerate the labeling process. You can now send predictions from a model run stored in Labelbox to a labeling project, making the pre-labeling workflow more seamless than ever.


With innovations surrounding large language models (LLMs) launching at a rapid pace, businesses and individuals alike are exploring new ways to put them to use. Fine-tuning an LLM is easy with Labelbox. Read this how-to guide to learn how.


Many AI teams struggle to produce training data that meets their quality requirements for successfully developing AI applications. Learn how @dialpad gained a 20% improvement in labeling quality when they made the switch to Labelbox.


Wrangling unstructured data can often be a massive roadblock for data science and AI teams. Learn how the Labelbox Connector for @databricks can help you quickly add structure and context to raw data with an efficient, integrated workflow.


Here's how you can make videos queryable and more useful for various tasks using foundation models such as Open AI Whisper, GPT 3.5, Meta's Timesformer and MPnet (using HuggingFace). Thread below:

🧵Last week, Stanford researchers introduced Alpaca 7B, a model fine-tuned from the LLaMA 7B model. Alpaca was trained on 52K instruction-following demonstrations generated from OpenAI's text-davinci-003. With Labelbox, you can get up to speed on this dataset today. 1/3

Selecting the right data to improve your model can be challenging, especially when you have an ocean of data to choose from. Learn how you can use embedding extractors from @huggingface with Labelbox Catalog to surface impactful data, fast.

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Using Labelbox and foundation models to generate custom embeddings and curate impactful data

In this guide, we'll show you how you can use foundation models, such as Hugging Face’s embedding extractors, ...


This dataset of chest X-rays of patients with COVID-19 can help AI builders train models that improve our knowledge of the effects of this on the human body. Explore the dataset on Labelbox Catalog.


Image generation AI has become popular in the past few months, but training a model to create images that look good is challenging. The LAION Art dataset was created for research aimed at identifying pleasant images. Explore this dataset on Labelbox.


LAION Art is a subset of LAION5B, estimated by a custom model trained on top of CLIP embeddings to choose aesthetically pleasing images.


Integrating the various tools in your tech stack is often challenging, as is adopting new solutions for building AI products. With @googlecloud's new expanded AI ecosystem, integrating solutions like Labelbox and others is now easier than ever.

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Building the most open and innovative AI ecosystem | Google Cloud Blog


Building AI models will require ML teams to adopt innovative solutions for MLOps, model training, and more. Today, @googlecloud announced an expanded partnership with Labelbox and other tools to make this process easier and faster for AI builders.

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


Our principal engineer, Matt Chambers, recently won an Academy Award for his scientific and technical achievements in render farm management system design. Read the post for more details and how he's applying his expertise to Labelbox products.

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

Matt Chambers was recognized for his achievements in render farm management system design with an Academy Award duri...


Forbes recognizes @gleanwork, @Rippling, @GetSTORD, @nova_credit, @Plaid, @brexHQ, @labelbox, @thrive, @DexterityRobots, and @secureframe as America's Best Startup Employers for 2023. Exploring new opportunities? These companies are a great place to start. https://www.forbes.com/lists/americas-best-startup-employers/?sh=4cca9a762ad7 https://twitter.com/i/web/status/1633550322970030082

We're proud to be on @Forbes's list of America's Best Startup Employers! Labelbox offers a remote-first culture, career growth opportunities, and excellent benefits, including unlimited PTO, free lunches, and more.


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Where is Labelbox headquarters?

Labelbox headquarters is located at San Francisco, California.

When was Labelbox founded?

Labelbox was founded in 2018.

How many employees does Labelbox have?

Labelbox has 50+ employees.

What is Labelbox's core business?

Labelbox is Computer Software company.

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