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Building a better AI data engine
AI practitioners regularly face a few common challenges: too much time spent building and maintaining tools and infrastructure, siloed AI development efforts, and fragmented processes to evaluate quality.
Building an effective "data engine" within your organization allows you to improve your model more quickly and reliably. Learn how a training data platform enables you to fuel that engine by harnessing active learning, collaboration, and full transparency into your ML workflow.
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Manu Sharma
CEO & Co-Founder, Labelbox
Peter Welinder
VP Product, OpenAI
Ready to automate your labeling?
Copyright 2023 - All Rights Reserved
Privacy FAQ | Privacy Notice | Cookie Notice | CCPA Notice | Terms of Use
Peter Welinder
VP of Product & Partnerships, OpenAI
*View terms and conditions that apply in order to qualify for promotion.
Higher quality data, faster
Your model is only as good as the quality of your data. Get the most out of your QA workflow with more granular and tangible insights, such as review time or average time per label, to dramatically improve labeling efficiency.
A single platform for data-centric AI
Leverage an end-to-end system that offers everything you need to improve custom model performance. From data curation to model training and diagnostic workflows, we help you build quality AI products faster than ever.
Fully configurable and flexible
We understand that every ML project might look a little different. Create custom workflows to fit your ML pipeline with UI features and APIs to easily curate, annotate, and evaluate model performance.
Tech-enabled BPOs and legacy labelings services are not a viable option for teams who want to decrease labeling costs and accelerate AI and ML projects in the long run. While they might help you get off the ground, these services don’t provide the resources to dramatically accelerate your production timeline. You’ll find that your labeling spend will quickly grow proportionally to the volume of data you’re labeling. More data quickly becomes more money spent which leads to exponential costs.
With Labelbox’s Catalog, you can quickly search and visualize all of your unstructured data in one place — unlocking active learning workflows to help identify the most impactful data to label next. With Labelbox’s Model, you’re able to manage every aspect of your training data preparation, model configuration, and model error analysis to iterate faster.
Tech-enabled BPOs primarily offers labeled data as a service. While this hands-off approach might appear convenient, it actually makes it hard to effectively manage and oversee labeling quality and review.
This lack of transparency into labeling QA and individual labeler performance metrics can mean you end up paying more for lower quality data. With Labelbox, your team has visibility into actionable metrics that help you achieve desired data quality while keeping human supervision costs low.
Plus, we’ll work with you to find the most efficient workflows to drive down labeling costs. Your labels can be reviewed, edited, and re-used at any time, enabling your team to prioritize rapid iteration and continuous improvement.
Labelbox Boost offers on-demand labeling teams and ML experts can help provide guidance every step of the way for everything from computer vision & NLP data to RLHF for LLMs. Our partner labeling services have been trained on using the latest automation techniques inside of Labelbox's platform and passes off all of these efficiency gains to you.
BPOs have teams that may seem large in number, but pay attention to the numerous reviews on their workforce quality. As an alternative, Labelbox Boost is here to help you create a smarter and more efficient labeling operation.
We carefully curate labeling teams and match your project with labelers who are already well-versed in your use case, taking you from initiation to production-scale labels in just a few days with maximum quality and accuracy. In addition, unlike most BPOs that charges per labeled object, Labelbox Boost bills per screen activity time so your costs stay the same even as projects scale.
Labelbox provides Model Foundry which allows teams to gain from the latest advances in foundation models to decrease labeling workloads as well as compare model performance among hundreds of models (open-source, closed-source, custom models).
Learn how your team can tap into foundation models that complement your human reviewers which will improve the speed and efficiency of your entire labeling operations.
Trusted by leading startups and Fortune 500 enterprises
Trusted by leading startups and Fortune 500 enterprises
*To qualify for the promotion, you need to commit to a new Enterprise subscription with Labelbox that totals at least $100,000 per year on an annual basis, with the contract being executed on or before Dec 31st, 2023. If you meet this requirement, you will be entitled to receive $50,000 in labeling services for every $100,000 in enterprise subscription (up to $1,000,000 in labeling services). Additionally, you'll get 1 million Labelbox Units (LBUs) for every $100,000 in annual contract value. For example, if your annual contract is $125,000, you'll receive $50,000 in labeling credits and 1 million LBUs. These labeling credits and LBUs will remain valid for the duration of your contract.