This is valuable information, of course, but there are plenty of great dives into this that are easily findable with a simple Google Search (i.e. I’ll avoid jumping into the weeds of how machines actually learn. What can machine learning do on mobile?īefore trying to dive into the tools and techniques that sit at the mobile + ML intersection, let’s quickly review the primary task categories in machine learning.
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These demo projects are invaluable for learning the intersecting skill sets and tools that make them possible.īut to transition from demo projects to production-ready apps that effectively leverage machine learning as a core component, it’s essential to clearly define and understand what machine learning features do, how they’re currently being used (and how to use them), and possible use cases for the future.Ī tall task, for sure, but I’m going to attempt to do that in this blog post. But in and of themselves, those ML tasks aren’t usually actionable inside a mobile experience.Īs such, we’ve seen a lot of interesting and high-performing demo projects that implement standalone ML features. It’s really cool and impressive to be able to point your phone’s camera at a scene and get a near-instant prediction that classifies an object, estimates a human’s position, or analyzes a block of text. From big players like Apple (Core ML, Create ML, Turi Create, Core ML Tools), Google (ML Kit, TensorFlow Lite), and Facebook (PyTorch Mobile), to startups like Skafos, the landscape of developer tools, educational resources, and incredible real-world applications continues to expand.īut from what we’ve gleaned working in this space over the past couple of years, there’s lingering uncertainty about how machine learning features can actually make user experiences better, more transformative, and more intuitive. Luckily, we’re starting to see more robust tools with increased institutional and community support.
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It would follow, then, that developers and engineers working with ML on mobile have to know how and when to code switch between model and application development.
Model training still generally happens on server-side ML frameworks (think TensorFlow, PyTorch, etc), while model inference can reliably take place on-device. The performance of a neural net and creating a fluid UI on mobile are (generally speaking) largely unrelated concerns.Īdditionally, mobile machine learning primarily takes place in two different contexts. From language and logic to the amount of specific knowledge needed to truly understand neural networks, the skill sets involved in mobile dev and machine learning can be disparate. Whether it was a WWDC session, an ARM whitepaper, or any number of insightful pieces about the future of ML, the concept of on-device machine learning is expanding in both theoretical and practical terms.īut the truth is, the buzz surrounding ML on mobile (Android and iOS) is only one variable in a nuanced equation.įor starters, the worlds of mobile development and machine learning are, in theory, quite far apart.
You’ve probably read or heard this sentiment somewhere. Image Source Machine Learning on Mobile: It’s cool, but…
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