Faizan Buzdar, Senior Director, Product Management, Box | @TheOtherFB, sits with John Furrier & Dave Vellante for Google Cloud Next 2018 from the Moscone Center in San Francisco, CA. #GoogleNext18 #theCUBE https://siliconangle.com/2018/07/27/dream-big-start-small-machine-learning-implementation-advises-box-exec-googlenext18/ Dream big, start small with machine learning implementation, advises Box exec As machine learning becomes more prevalent in modern business, many are wondering where to start in their implementation of the progressive and promising technology. And while some business leaders may possess lofty ideas and dreams for machine learning’s ability to embed intelligence into computers, it’s easier to start smaller and have realistic expectations to begin a true digital transformation within a company. “My candid advice is: ‘Don’t start with curing cancer,'” said Faizan Buzdar (pictured), senior director of Box Inc. “Start with something where there is some manual data being added … at scale. And take those scenarios … and now apply machine learning to that.” Buzdar spoke with John Furrier (@furrier), co-host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Google Cloud Next event in San Francisco. They discussed machine learning, examples of implementing machine learning, and ways Box is partnering with Google. (* Disclosure below.) Machines managing data for cost-effective results One example of applying machine learning tech for manual data entry is within a company such as those in the ride-sharing business, according to Buzdar. For ride-sharing, a company might scan 50,000 driving licenses in a given city, all of which have to be manually entered into the database by employees. But what if machine learning could do that work for them? “Previously, it would take a month for you to get the data entered for 50,000 driving licenses; now you can do it in 50 minutes,” Buzdar said. “For us, I think the biggest thing there is is: ‘How do we enable companies to experience machine learning faster?'” There’s also the question of human error versus machine learning error, but Budzar isn’t too concerned. While humans have a minimum five percent to 30 percent error rate, machine learning is still much cheaper with similar rates, he stated. “The reason that I love replacing the data entry portion is that machine learning is never 100 percent, but to the validation process it still looks like kind of the same thing,” Budzar concluded. “You still saved all of your money.” Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Google Cloud Next event. (* Disclosure: Google Cloud sponsored this segment of theCUBE. Neither Google Cloud nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)