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Introduce Your Data To AI

Vision AI Made For You

Solution Overview

Emerald AI

Who knows your data better than you?

Emerald AI truly strives to make the adoption and integration of AI into your workflow as easy as possible. With No expertise you can Build, Train, and Maintain your Vision AI models with little to no effort. No need for AI Teams or Data Scientists when you can build it yourself.


Building Your AI

It's as easy as...

Try out some Beta Features

The Emerald AI Team is happy to announce that we are ready to move into BETA.

We are looking for small to mid sized companies to provide constructive feed back for this game changing platform. Please provide your use case for consideration.
Early Access

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Emerald AI's Approach

Emerald uses Bayesian Meta-Learning which, has seen rapid improvement recently[1-2] and we are ready to move on from benchmarks[3-4] to real use cases with the launch of a closed beta by the end of this year. Our methods are also extremely computationally efficient which, allows for rapid feedback loops. One advantage of the accurate uncertainty estimates we have is that we can provide that information in our data visualizations on the produced labels in a matter of minutes, from labeling, to training, to whole dataset application. When attribution is used for dataset balancing, the automated attribute labels do not need to achieve extremely high accuracy or certainty before the results are acceptable. This is because deep-learning models are generally robust to slightly imbalanced data. Finally, by utilizing external data sources, we can search and provide augmented data to balance and improve datasets based on underrepresented data attributes.

[1] C. Nguyen, T.-T. Do, and G. Carneiro, “Uncertainty in Model-Agnostic Meta-Learning using Variational Inference,” arXiv:1907.11864 [cs, stat], Oct. 2019, Accessed: Jul. 15, 2021. [Online]. Available:

[2] H. B. Lee et al., “Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks,” presented at the International Conference on Learning Representations, Sep. 2019. Accessed: Jun. 29, 2021. [Online]. Available:

[3] X. Zhai et al., “A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark,” arXiv:1910.04867 [cs, stat], Feb. 2020, Accessed: Jul. 01, 2021. [Online]. Available:

[4] E. Triantafillou et al., “Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples,” arXiv:1903.03096 [cs, stat], Apr. 2020, Accessed: Jun. 29, 2021. [Online]. Available:

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