I’ve always believed that technology should be a force for good.
In 2012, I joined the founding team at VolunteerLocal, a Des Moines-based software company designed to automate the volunteer signup and scheduling process for events and nonprofits. Over the last nine years, we scaled that business to thousands of users impacting millions of volunteers around the globe.
With a fantastic team at the helm, I knew it was time for me to pursue a new challenge.
If you’ve ever tried to explain how computer vision works to your friends, family or colleagues, you probably know that it can be hard to do. This is especially true if you start using the common jargon (e.g. neural networks, hyperparameters, machine learning), as these terms can sometimes be counterproductive. Instead of adding clarity, they complicate the matter further.
We know, however, that computer vision is already touching all of our lives, from social media to transportation, online shopping to photos stored in the cloud. Believe it or not, most of us interact with machine learning algorithms everyday.
(But the AI Machines Don’t Have To Be)
Computer vision, on the whole, is an ambitious undertaking.
We are developing technology that can see the world as we see it — to recognize simple objects like trees and croissants, and more complex occurrences like oil and methane leaks. Today, models can even read license plates and receipts. Computer vision is already changing our world, its applications both expansive and breathtaking.
When we talk about the importance of representative data, we usually mean it in the context of the environment around the object(s) we are teaching a model to detect. For…
It’s no secret that building a computer vision model on your own is hard work. It requires wrangling together different platforms, open-source tools, and developer notebooks to create a functioning machine learning pipeline. And that’s just the beginning; after you have a trained model you still have to do the engineering work to deploy and maintain it in the wild.
I speak with developers every day who have spent countless hours crafting these workflows — and while they are undoubtedly impressive, they are also labor-intensive and require frequent maintenance. What is worse, when any of these disparate systems break (and…
Developing, deploying and optimizing computer vision models used to be a cumbersome, painful process. With Roboflow, we sought to democratize this technology, which (first and foremost) meant knocking down the barriers that we perceived were preventing everyday people from exploring and implementing computer vision in their work and daily lives.
The natural result of this undertaking was a true “end-to-end” solution, a product that enables users to start with a set of raw images, and in the span of an afternoon, create a fully trained computer vision model. The only necessary ingredients to this process are a laptop, a wifi…
Building at Roboflow