Valohai for IoT & Robotics

Scaling ML development for a world of intelligent devices.

The Valohai platform empowers teams to build scalable machine learning workflows that integrate seamlessly with any existing tools.

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Enabling teams to scale ML development – without giving up on flexibility.


Shaping the future of human-machine interaction.


Building the best robotic pool cleaners in the world.

Path Robotics

Introducing truly autonomous welding.


Leading the way in predictive maintenance.


Scale experimentation with ease on any hardware

Valohai’s smart orchestration makes spinning up as many experiments as you need easy on any cloud or on-premise environment.


Unify workflows across individuals and teams

Valohai’s knowledge repository stores all experiments, metrics and models so you and your team are working from the same foundation.


Handle big and unstructured data with ease

Valohai is built to handle data in any format and from any source. In addition, Valohai’s data caching removes unnecessary data transfer.


Integrate with any existing tools

Valohai is built with open APIs, which makes connecting existing workflows easy. We know every use case is slightly different.


KONUX leads the way in predictive maintenance

Utilizing machine learning for predictive maintenance is an area that holds great promise for many industries. KONUX has been a trailblazer in this domain, and they’ve been internationally recognized, including in CBInsight’s AI 100 in 2020 and 2021.
Read full case study ➜


Urban waterways: The next generation of autonomous transportation

With the promise of relieving strain on the transport network in maritime cities, Finnish software powerhouse Reaktor set to build a solution for future waterways. The Valohai platform empowered Reaktor to increase the speed of model development almost tenfold.
Read full case study ➜

Blog post

MLOps for IoT and Edge

There's a new wave of automation being enabled by the combination of machine learning and smart devices. With the complexity of use cases and amount of devices increasing, we'll have to adopt MLOps practices designed for IoT and edge.
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Andres Hernandez
Large-scale experimentation tends to be tricky because you’ll need to manage cloud resources, and mistakes can be quite costly. With Valohai, though, that stress is gone, and we can focus on the actual data science. The version control of all parts of an experiment, from code to data to environment, allows for systematic research, which can be reviewed months later. On top of that, when we do all of our experiments on Valohai, it’s easy to promote them for production use later on.
Andres Hernandez, Lead Data Scientist, KONUX
KONUX combines IIoT and machine learning to transform railway operations. They’ve been internationally recognized, including in CBInsight’s AI 100 in 2020 and 2021.