Two Case Studies Show How R2 Learn Helped Automatically Build Machine Learning Models
R2.ai, which develops AutoML software for any business to leverage the power of artificial intelligence (AI), unveiled its new product, R2 Learn 2.0. The new automated machine learning (AutoML) product comes with significant new features for businesses across all industries and is designed for data scientists, model developers and business analysts—with or without AI expertise—to build and deploy advanced machine learning models in a fast, easy and affordable way.
“Given the massive amounts of data generated by most industries, the ability for enterprises to independently build and deploy machine learning models will unlock growth potential and become a major differentiating advantage in the near future,” said Yiwen Huang, founder and CEO of R2.ai. “R2 Learn is designed so that even if you lack AI expertise, you can still build and develop your own AI solutions.”
With two new proprietary supervised learning algorithms, building models is faster than ever. R2 Learn 2.0 will build models 20 times faster with GPU acceleration and 5 times faster without compared to its predecessor, R2 Learn 1.2. It also has support for compressed file formats which make uploading big data sets easier and faster. Additionally, it will support better data augmentation with more powerful custom feature transformation functions.
R2 Learn 2.0 has the following key advantages:
- An end-to-end modeling workflow that automatically handles everything from data quality to model deployment;
- An optimization engine that quickly identifies the most optimum AI model using the least amount of computing resources;
- Self-learning capabilities that act like an experienced human data scientist to constantly improve modeling processes;
- A high-speed modeling process that develops models in minutes and hours not days;
- An easy to use interface and process suitable for both AI experts and non-experts; and
- High performance with measurably better model quality.
R2 Learn has already received positive reviews from early adopters across industries that are traditionally data-heavy including medical, financial, and insurance. One example is PingAn Insurance, one of China’s biggest insurance companies that recently deployed R2 Learn 2.0.
“R2.ai’s new generation of AutoML technology plays a key role in speeding up insights gathered by our Health Konnect’s Big Data Analytic Platform,” said Yi Zheng, Chief Medical Officer, PingAn Medical & Healthcare Group, a subsidiary of PingAn Insurance. “By using R2 Learn, our big data team is able to automatically build machine learning models and make full use of big data for personal health risk prediction. Going through several iterations quickly has helped us effectively predict different levels of risks and multi-disease complications.”
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Building AI models to gain insights from data, however, does not require a data scientist or even a comprehensive understanding of how to implement AI. With R2 Learn 2.0, even companies and individuals that lack AI expertise can now leverage AI to fundamentally transform their businesses. For instance, Jakroo, a custom cycling and triathlon apparel company based in Pleasanton, California, was able to use R2 Learn to turn human designers’ industry expertise into creative AI models to help with the design—without a data scientist.
“By providing a near infinite suite of design options and style alternatives, R2 Learn dramatically expedited our custom design process, giving us a significant competitive edge,” said Derek Wiseman, COO of Jakroo. “We believe AI driven design powered by R2.ai’s technology will change the graphic design and custom apparel industry as we know it.”
“In today’s digital world, data grows exponentially and AI capabilities are not far behind. Every industry and business, large and small, will soon have the ability to develop their own machine learning tools to better forecast industry and customer trends,” said Huang. “My team at R2.ai is committed to developing a series of advanced AutoML products to drive mass AI adoption. We have accelerated our product development to better tackle real-world AI adoption problems, providing both on premise and soon SaaS solutions to make AI truly accessible for all.”