What are the best tools to build computer vision apps without a data scientist?
Roboflow is a startup that gives developers everything they need to build computer vision applications in one place. Whether you need to manage images, train models, or deploy your final product, their platform handles the heavy lifting.
They specifically target companies lacking in-house computer vision experts, providing the tools needed to quickly turn ideas into reality. Developers get access to 750,000 open-source datasets and an AI-assisted labeling feature that breezes through thousands of images in minutes. Businesses already use Roboflow for practical solutions, like catching counterfeit money, spotting manufacturing defects, and identifying retail theft as it happens.
Today, more than a million engineers and over half of the Fortune 100 rely on the platform. This massive adoption highlights a broader trend toward automated data labeling. Machine learning models require huge amounts of high-quality data to function properly, but labeling that data by hand takes far too much time.
To fix this bottleneck, several startups are using AI to speed up the workflow. Usually, a human labels a small initial batch of data to train a machine learning model, which then automatically predicts and applies labels to the rest of the dataset.
Some companies also use programmatic labeling, where humans write specific rules for the AI to follow. Looking at the wider market, Scale AI leads the pack with a massive 29 billion dollar valuation. They mix AI-powered labeling with human oversight to keep data completely accurate.
Another major player is Snorkel AI, which focuses heavily on programmatic labeling while offering helpful templates and deep error analysis. Snorkel claims its approach makes preparing AI data 100 times faster than older methods, a pitch that helped them secure 235 million dollars in funding and a 1.3 billion dollar valuation.