It’s time to let loose and break out the AI transformations. We are extremely excited to announce that Dataloop has raised a significant investment of $16M in funding following the completion of an $11M Series A round and a previously undisclosed $5M seed round. With this investment we’ll be able to continue to grow our team and expand our activities in the field of AI data annotation and management in additional global markets that include the US and Europe.
In this blog post, we’ll be focusing on the market potential and what we plan to do with this funding. So if you’re interested in reading more about the investment itself, which was raised amidst the coronavirus outbreak, you can find all the details in this press release.
We’re living in unprecedented times and today, more than ever, there is an increasing need to accelerate the development of AI capabilities across multiple verticals. This is backed by research that shows the AI market projected to become a $190 billion industry by 2025.
Overcoming data labeling limitations to unleash the full potential of the AI market
At Dataloop, we are excited to play a part in unleashing this market’s full potential and believe that by weaving human and machine intelligence, we can accelerate vision AI and give businesses new opportunities to grow and expand their offerings.
That said, there are some major challenges to overcome on the way to achieving widespread adoption of AI technologies. We believe that the main barrier to adopting this technology is data. Preparing data for AI is a challenging, expensive, time-consuming, and often error-prone process. As the first stage of AI model training, this process involves the labeling of vast amounts of raw, unstructured data from cameras, sensors, and other equipment collected by organizations, and the processing of this data into usable data output. Or in other words, efficiently labelling data at scale. For this reason, it’s no surprise that about 96% of companies face problems with data labeling when it comes to AI implementation and production.
At Dataloop, we’re well aware of this barrier and have witnessed many organizations struggling to move their AL and ML projects into production as a result of data labeling limitations and a lack of real-time validations. Most of these organizations state that their biggest challenges are related to data quality, data labeling, and building model confidence. This makes sense when you consider that accurately-labeled data is a critical prerequisite to ensuring ML systems establish reliable pattern recognition models, and are therefore the foundation of every AI project. It also makes sense that the lack of such data can severely undermine highly-complex AI-based projects by invalidating predictive models. Moreover, even when these organizations successfully move beyond this phase, they still face the additional challenge of managing AI in production.
That’s where we come in. We’ve spent the last few years developing a leading and customizable SaaS platform for AI data annotation and data management that weaves together human and machine intelligence to accelerate vision AI.
Our proprietary platform covers the entire data preparation cycle, from training and data labeling, through to automating data ops, customizing production pipelines, and powering enterprises successfully in production. This platform also consistently feeds real-time data back to human counterparts, as by keeping humans in the loop, our algorithms can create more accurate and reliable predictions in less time. This, together with the simultaneous streamlining of workflows using automated annotation tools, helps organizations overcome data challenges to successfully deploy AI in production while focusing on their core business.
Overcoming roadblocks to provide next generation data management tools
So what does the future hold for Dataloop and the AI industry? And how do we intend to use this investment wisely so that we continue to offer an advanced AI data labeling and management platform that brings real value and a competitive advantage to our customers?
Well, with this investment we are committed, together with our partners, to continuously developing our next-generation data management tools and to making machine-learning systems more accessible, affordable and scalable for organizations around the world. This involves finding more ways to overcome the obstacles on the way to widespread AI technology adoption so that we can do our part in transforming the AI industry and meeting the rising demand for innovation in additional global markets.