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.
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.
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.