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.
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.
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.
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.
Today’s machine learning teams consist of people with different skill sets. There are a bunch of different roles that are needed, but today I am going to talk about the two key roles that I get asked about the most: machine learning researcher / data scientist vs. machine learning engineer.
Smart recommendation in apps and websites is not an additional feature that differentiates top industries from others. Most users take for granted that they will be suggested products that they like. Collaborative filtering has been widely used to predict the interests of a user by collecting preference and tastes information from many users. It is often combined with content-based filtering, especially for tackling the cold-start problem. In the following tutorial, we will walk through building a clothes detection system which forms the basis of a robust fashion recommender engine.
In the age of technology, conventional methods are being automated, and computers are taking over. Similarly, for energy distribution, smart grids are replacing traditional energy distribution grids which allow efficient distribution and demand-side management.
Valohai, a machine learning (ML) platform-as-a-service company, has raised $1.8M in funding to help international companies accelerate machine learning development and scale their model deployment. The round was led by Nordic seed stage investment company Superhero Capital, with participation from Reaktor Ventures and Business Finland, the Finnish Funding Agency for Innovation.
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% and the overdetection rate dropped dramatically.
My name is Ruksi, I'm a machine learning engineer at Valohai.
We at Valohai are building machine learning platform-as-a-service. Underneath this mouthful of a buzzword we are actually trying to solve a real world problem I've seen being tackled over and over in dozens of big and small organizations applying or researching machine learning.