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See what success with Valohai looks like.

A high-performance visual search engine

Nyris is developing a high-performance visual search engine that understands the content of an image. The visual search engine works as an easy-to-use API that companies can use to inject visual search as a part of their solution.

Improving smart-forestry through machine learning

CollectiveCrunch’s main product, Linda Forest, helps the forestry industry better understand and target the raw materials they are buying for optimizing the resource efficiency.

AI powered legal aid for social media harassment

Someturva is able to attract children and teenagers into seeking help for offenses in social media, classify the cases in crimes and non crimes and give sound advice as to how to proceed in such situation.

Skillup had machine learning version control from the beginning

Skillup develops machine learning models to build and maintain a marketplace for professional trainings.

Real world example of deep learning: Sexual abuse material detection

Two Hat Security builds and sells a system for automatically detecting sexual abuse material in video material in darknet or other hard to reach parts of the internet.

Urban waterways: The next generation of autonomous transportation

With the promise of relieving strain on the transport network in maritime cities using Artificial Intelligence and autonomous driving technology, Finnish software powerhouse Reaktor set to build a solution for future waterways. As part of the project, the Valohai platform empowered Reaktor to increase the speed of model development almost tenfold, making it possible to train the self-steering algorithm over night beating the initial training time of one week.

How data scientists are changing nature conservation with deep learning

Jacques Marais used machine learning to scan Africa’s elephant population from aerial infrared and color images taken from a plane. The built models were trained first in 2015 with local GPU hardware in three weeks. When the models were retrained in 2017 with the Valohai platform the work was completed in three days while the detection accuracy increased from 56% up to 67% while the overdetection rate dropped dramatically.

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