Software : Information Technology : MLOps
Landing AI is pioneering the next era of AI in which companies with even limited data sets can realize the business and operational value of AI and move AI projects from proof-of-concept to full scale production. Guided by a data-centric AI approach, Landing AI’s flagship product is LandingLens, an enterprise MLOps platform that offers to build, iterate, and operationalize AI powered visual inspection solutions for manufacturers. With data quality being key to the success of production AI systems, LandingLens enables users to achieve optimal data accuracy and consistency. Founded by Dr. Andrew Ng, co-founder of Coursera, former chief scientist of Baidu, and founding lead of Google Brain, Landing AI is uniquely positioned to lead the development of AI from a technology that benefits a few to a technology that benefits all.
Breaking Down CB Insights’ Advanced Manufacturing 50 for 2022
Exponential Industry’s thoughts on CB Insights’ Advanced Manufacturing 50. Data lakes drive MLOps into manufacturing processes. 3D printing companies continue to merge.
Andrew Ng’s Landing AI aims to help manufacturers deploy AI vision systems
Today, the company announced its LandingEdge, which customers can use to deploy deep-learning based vision inspection to their production floor. The company’s first product, Landing Lens, enables teams, who don’t have to be trained software engineers, to develop deep learning models. LandingEdge extends that capability into deployment, Yang says. “Strategically, manufacturers start AI with inspection,” Yang said. “They use cameras to repurpose the human looking at the product, which makes inspection more precise.
LandingEdge attempts to simplify the platform deployment for a manufacturer. Typically users set up a method to “train” their vision system by plugging the LandingEdge app into programmable logical controller and cameras. The PLC continuously monitors the state of cameras and the vision system itself.
"In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In #manufacturing, you might have 10,000 manufacturers building 10,000 custom #AI models."@landingAI #Industry40 https://t.co/z7BvgDeJsE— David Rogers (@doclrogers) February 10, 2022
Why AI software companies are betting on small data to spot manufacturing defects
The deep-learning algorithms that have come to dominate many of the technologies consumers and businesspeople interact with today are trained and improved by ingesting huge quantities of data. But because product defects show up so rarely, most manufacturers don’t have millions, thousands or even hundreds of examples of a particular type of flaw they need to watch out for. In some cases, they might only have 20 or 30 photos of a windshield chip or small pipe fracture, for example.
Because labeling inconsistencies can trip up deep-learning models, Landing AI aims to alleviate the confusion. The company’s software has features that help isolate inconsistencies and assist teams of inspectors in coming to agreement on taxonomy. “The inconsistencies in labels are pervasive,” said Ng. “A lot of these problems are fundamentally ambiguous.”
Landing AI Secures Funding to Unlock Power of Small Datasets, Unleashing Next Era of AI
Landing AI, led by artificial intelligence visionary, Andrew Ng, developed LandingLens™, a fast, easy to use enterprise MLOps platform. It applies AI and deep learning to help manufacturers solve visual inspection problems, find product defects more reliably, and generate business value.
“You don’t always need big data to win with AI. You need good data that teaches the AI what you want it to learn,” said Ng, Founder & CEO of Landing AI. “AI built for 50 million data points doesn’t work when you only have 50 data points. By bringing machine learning to everyone regardless of the size of their data set, the next era of AI will have a real-world impact on all industries.”
The data-centric approach of Landing AI is also key to making LandingLens fast and easy-to-use. The process of engineering the data, instead of the AI software, gives an efficient way for manufacturers to teach an AI model what to do. Domain experts, not just AI experts, can now build an AI system, and take it to production. For example, rather than needing to write pages of code to train a neural network, a domain expert can do it with a few mouse clicks. This no code/low code data-centric platform enables new users to build advanced AI models in less than a day. Vision inspection projects that used to take over a year can be executed in just weeks using LandingLens.