On Monday, Landing AI (where I’m CEO) announced the close of a $57 million Series A funding round. The investment enables the company to continue building its data-centric MLOps platform for computer vision, with a focus on manufacturing visual inspection.
Studies estimate that AI will create trillions of dollars of value, and machine learning already has changed the trajectory of consumer-internet companies like Google and Facebook. Yet the technology has barely penetrated most other industries. Making AI work in more traditional industries will require a different recipe than internet companies use. I explained why this week at Fortune’s Brainstorm A.I. event, pictured below.
Datasets are much smaller. I once built a face recognition system using about 350 million images. But when I asked people in the manufacturing industry how many images they had of each defect they wanted to recognize, 50 or fewer was the most common answer. Techniques developed for learning from hundreds of millions of examples will struggle to work with only 50. But the situation improves if you choose those examples well. Data-centric AI tools can help you get there.
Applications are more diverse. If we took all current and potential machine learning projects and sorted them in decreasing order of value, we might find that the “head” of the distribution comprises applications like a large company’s web search engine, online ad system, or product recommendation engine. This is followed by a “long tail” of applications that have lower value individually but massive value in aggregate. As a community, we’ve figured out how to organize dozens or hundreds of engineers to build these large applications, some of which can generate over $1 billion of value. But this recipe doesn’t work for other industries where applications are more heterogeneous and where each of 10,000 machine learning models generates $1 million to $5 million each.
For example, in manufacturing, each plant makes a different product, and thus will need a different trained model to detect defects. In healthcare, every hospital codes its electronic health records (EHR) differently. Rather than a single monolithic model to read every hospital’s EHR, each hospital needs a system trained on its own data. The total value of these applications is enormous. But how can any company help build, deploy and maintain 10,000 custom models without hiring 10,000 machine learning engineers?
This “long tail” problem helps to explain why many proof-of-concept implementations and demos don’t make it into production. While a team of engineers can build a one-off application, we still need better tools to make this type of work scalable and economically viable.
Landing AI is building tools to make it fast and easy for manufacturers to engineer the data so as to train, deploy, and maintain their own computer vision systems. This design pattern addresses the widespread problems of small datasets and diverse applications. If you’re working in a sector other than manufacturing, consider if your sector has a long tail of applications and if building an MLOps platform to let customers do their own customization — as Landing AI is doing in manufacturing — might advance machine learning in your industry.