Valohai Blog

Building Trust in AI Applications

[fa icon="calendar'] Oct 8, 2018 7:45:00 AM / by Joanna Purosto

unsplash-logo Aarón Blanco Tejedor


All of us have seen those fear mongering headlines about how artificial intelligence is going to steal our jobs and how we should be very careful with biased AI algorithms. Bias means that the algorithm favors certain groups of people or otherwise guides decisions towards an unfair outcome. Bias can mean giving a raise only to white male employees, increasing criminal risk factors of certain ethnic groups and filling your news feed only with topics and point of views that you are currently consuming – instead of giving a broad, balanced view of the world and educating you.

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Valohai and Microsoft Join Forces in Deep Learning for Enterprises

[fa icon="calendar'] Oct 3, 2018 8:23:00 AM / by Eero Laaksonen posted in Microsoft, Press Release, Partnerships, Machine Learning Tools

Valohai and Microsoft cross lightsabers in the battle for artificial intelligence, through Microsoft’s global ScaleUp Program.

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Speeding up Deep Learning with PowerAI

[fa icon="calendar'] Oct 1, 2018 11:10:28 PM / by Fredrik Rönnlund posted in Machine Learning Tools, Power AI, On-premise training, Partnerships, IBM

Just lately we’ve been playing around with IBM PowerAI in order to ensure our customers can leverage it in large-scale on-premise training. PowerAI in itself is IBM’s solution for deep learning consisting of software and hardware to help you quickly train deep learning models. Today we’re happy to announce that Valohai fully supports PowerAI and our customers can start using it!


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Two Years of Democratizing AI

[fa icon="calendar'] Sep 28, 2018 3:16:33 AM / by Eero Laaksonen posted in CTO, Machine Learning Infrastructure, Valohai

Valohai is turning 2 years old in three weeks. The paperwork was done on October 16th, 2016. It’s been a thrilling ride so I’ll take this chance to write a few words about why we really started this company.
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Data Scientists Are Rocket Surgeons Stuck With Stone Age Tools 📠

[fa icon="calendar'] Sep 7, 2018 11:17:59 AM / by Fredrik Rönnlund posted in Machine Learning Tools, Machine Learning Infrastructure, Software Engineering

A software engineer’s view at data science.

If developers used to be the rock stars of the dotcom era, Data Scientists are quickly overtaking them as the new Whitesnake cover bands of the 2020s. Although both might be sporting the same hobo beards, Data Scientists are getting their work done with just sticks and stones as their tools while us Software Engineers have every tool in the universe.

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Level Up Your ML Code from Notebook to Production

[fa icon="calendar'] Aug 24, 2018 12:16:52 PM / by Aarni Koskela posted in Jupyter Notebook

Developing a machine learning model for a new project starts with certain common groundwork and exploration, to understand your data and figure out the approaches to try. A popular choice for this groundwork is Jupyter, an environment where you write Python code interactively. In Jupyter notebook's cells you can evaluate and revise and it is an attractive, visual choice (and many times the right choice) – for this step of data science work. Since Jupyter kernels, the processes backing a notebook’s execution, retain their internal state while the code is being edited and revised, they’re a highly interactive, fast-feedback environment.

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The Importance of Reproducibility

[fa icon="calendar'] Jul 11, 2018 1:04:47 PM / by Aarni Koskela posted in Machine Learning Tools, Reproducibility

Reproducibility and replicability are cornerstones of the scientific method. Every so often there’s a sensationalized news article about a new scientific study with astounding results (for instance, we’re looking forward to seeing what’s hot at ICML 2018 – we’re attending, come say hi!) – and it’s not uncommon in these cases that there’s no way for other fellow scientists to verify these results by themselves, be it due to missing or proprietary data, or faulty methodologies. This, naturally, casts shade over the entire study in question.

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Top 49 Machine Learning Platforms – The Whats and Whys

[fa icon="calendar'] Jul 6, 2018 8:12:26 AM / by Ruksi posted in Machine Learning Tools, Machine Learning Infrastructure

If machine learning is a team sport, like I so frequently hear, machine learning platforms must be the playing fields. And to up your machine learning game, you must have the proper environments to do it.

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Urban Waterways: The Next Generation of Autonomous Transportation

[fa icon="calendar'] Jun 28, 2018 1:48:47 PM / by Toni posted in Case

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.

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Machine Learning Infrastructure Explained to Business People

[fa icon="calendar'] Jun 20, 2018 12:00:00 PM / by Joanna Purosto posted in Machine Learning Infrastructure, Machine Learning Team

After spending two days at the AI Summit fair in London and having several conversations with people from different business backgrounds, I wanted to clarify why machine learning infrastructure is one of the biggest things to concentrate on when building production level machine learning models.

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