This week, see the latest in how AI & ML are impacting the production process for a popular denim brand, the preservation of wildlife, overall project efficiency and allowing IT teams to go on autopilot.
By John Paul, contributing writer for Venturebeat.com
As ubiquitous as machine learning is in the enterprise, Levi’s might not be the first brand that comes to mind when you think of AI smarts. As a company that has been producing jeans and other denim apparel since 1853, Levi Strauss & Co. seemed to be doing just fine without the intervention of neural networks and machine learning algorithms. But like so many large companies, Levi’s has found plenty of uses for AI technology, from automating mundane tasks and analyzing denim-related data sets to helping its designers create new denim jacket designs.
By IT Trailblazers LLC, contributing writers for Linkedin.com
A team of experts in artificial intelligence and animal ecology have put forth a new, cross-disciplinary approach intended to enhance research on wildlife species and make more effective use of the vast amounts of data now being collected thanks to new technology. Their study appears today in Nature Communications.
By Packt Publishing, contributing writers for Techtarget.com
Realizing a return on investment for data science projects often relies on data scientists’ ability to fail quickly and then recover to deliver finished projects in a timely fashion. However, many of these projects take too much time and don’t succeed.
By MIT Technology Review Insights, contributing writers for Technologyreview.com
Sercompe Business Technology provides essential cloud services to roughly 60 corporate clients, supporting a total of about 50,000 users. So, it’s crucial that the Joinville, Brazil, company’s underlying IT infrastructure deliver reliable service with predictably high performance. But with a complex IT environment that includes more than 2,000 virtual machines and 1 petabyte—equivalent to a million gigabytes—of managed data, it was overwhelming for network administrators to sort through all the data and alerts to figure out what was going on when problems cropped up. And it was tough to ensure network and storage capacity were where they should be, or when to do the next upgrade.
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