Apple describes its mobile devices as designed in California and assembled in China. You could also say they were made by the App Store, launched a decade ago next month, a year after the first iPhone.

Inviting outsiders to craft useful, entertaining, or even puerile extensions to the iPhone’s capabilities transformed the device into the era-defining franchise that enabled Uber and Snapchat. Craig Federighi, Apple’s head of software, is tasked with keeping that wellspring of new ideas flowing. One of his main strategies is to get more app developers to use artificial intelligence tools such as recognizing objects in front of an iPhone’s camera. The hope is that will spawn a new generation of ideas from Apple’s ecosystem of outsourced innovation.

“We have such a vibrant community of developers,” Federighi says. “We saw that if we could give them a big leg up toward incorporating machine learning into their apps they would do some really interesting things.”

He illustrates the point with a demo of an iPad app for basketball coaches called HomeCourt. You don’t have to be a pro; using the app is as easy as pointing an iPad’s camera at action on the court. Then the tricky stuff happens automatically. HomeCourt uses the support for machine learning added to Apple’s mobile operating system last year to analyze the video. The app tracks each time a player shoots, scores, or misses, and logs the shooter’s location on the court. Each event is indexed so a particular play can later be viewed with a single tap.

HomeCourt is built on tools announced by Federighi last summer, when he launched Apple’s bid to become a preferred playground for AI-curious developers. Known as Core ML, those tools help developers who’ve trained machine learning algorithms deploy them on Apple’s mobile devices and PCs.

At Apple’s Worldwide Developer Conference on Monday, Federighi revealed the next phase of his plan to enliven the app store with AI. It’s a tool called Create ML that’s something like a set of training wheels for building machine learning models in the first place. In a demo, training an image-recognition algorithm to distinguish different flavors of ice cream was as easy as dragging and dropping a folder containing a few dozen images and waiting a few seconds. In a session for developers, Apple engineers suggested Create ML could teach software to detect whether online comments are happy or angry, or predict the quality of wine from characteristics such as acidity and sugar content. Developers can use Create ML now but can’t ship apps using the technology until Apple’s latest operating systems arrive later this year.

Apple is far from the first tech company to release software to help developers build machine learning models. Facebook, Amazon, Microsoft, and Google have all done so, with Google’s TensorFlow most popular. Federighi claims none easily fit into an app developer’s regular workflow, limiting machine learning’s potential. “We're really unleashing this capability for this vast developer community,” he says. Create ML is built on top of Apple’s Swift programming language, introduced in 2014 and popular in some developer circles for its ease of use.

Simplifying can bring limitations. Create ML looks useful, but creating complex or unique uses of machine learning requires building something from scratch, says Chris Nicholson, CEO of Skymind, which helps companies with machine learning projects. Predicting events over time, like what a customer will buy next, typically requires something bespoke, he says. “What will make apps stand out is a fully custom, proprietary model,” says Nicholson.