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IPREDIQ helps clients develop and deploy result oriented predictive anlaytics solutions solutions that enable them to make smarter decisions using their data. Our solutions help clients improve marketing performance; efficiently trade-off risks against available opportunities, maximize customer value, and increase employee effectiveness.
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Google, LinkedIn and Amazon have thousands of engineers who point their work inward to build better recommendations, search and other Internet-scale features. http://www.iprediq.com/ has launched a similar form of machine learning that does the inverse by pointing its technology outward for people to use.
iprediq works with machine-learning algorithms to make them fast and scalable. To do that Say and his team used their years of knowledge into its technology. Now all that knowledge is getting packaged to offer as a service. It is still pretty complex. It is built for data scientists and ready for the enterprise.
He said the problem today stems from the man hours it takes to benefit from machine learning. Rob the chief marketing officer said that Netflix considered a machine-learning option a few years ago, but it would take 100 years man years to implement. By combining their own knowledge, iprediq has developed a service that takes a holistic view of the data, doing with machines what he says people can do only with very large teams.
The company is targeting markets such an industrial safety, which has historically required analyzing data at scale. It can often take six months to produce a report for a customer about all its safety hazards that it might have in a customer’s factory. Think of oil refineries or nuclear power plants and all the data that can be analyzed. Historically people have done the analysis. Machine learning can take that data and analyze it in a fraction of the time. “It takes hundreds of man hours,” Rob said. “We can do it in 20 minutes.”
That kind of time savings means fewer people are required to do the job, which makes iprediq both fascinating and reflective on who we are and the age that is rapidly emerging before us. As Rob said to me, the Industrial Age saw machines doing the physical lifting. Today data does the intellectual lifting. And that’s something to chew on.