Mobile developers have a lot to gain from revolutionary changes that on-device machine learning can offer. This is because of the technology’s ability to bolster mobile applications—namely, allowing for smoother user experiences capable of leveraging powerful features, such as providing accurate location-based recommendations or instantaneously detecting plant diseases.
This rapid development of mobile machine learning has come about as a response to a number of common issues that classical machine learning has toiled with. In truth, the writing is on the wall. Future mobile apps will require faster processing speeds and lower latency.
You might be wondering why AI-first mobile applications can’t simply run inference in the cloud. For one, cloud technologies rely on central nodes (imagine a massive data center with large quantities of storage space and computing power). And such a centralized approach is incapable of handling processing speeds necessary to create smooth, ML-powered mobile experiences. Data must be processed on this centralized data center and then sent back down to the device. This takes time and money, and it’s hard to guarantee data privacy.
Having outlined these core benefits of mobile machine learning, let’s explore in more detail why, as a mobile app developer, you’ll want to keep your eyes peeled for the incoming on-device ML revolution.
Mobile app developers know that high latency can be the death knell for an app, regardless of how strong the features are or how reputable the brand is. Android devices have had latency issues with a number of video apps in the past, resulting in viewing experiences with out-of-sync audio and video. Similarly, a social media app with high latency can make for an extremely frustrating UX.
Performing machine learning on-device is becoming more important precisely because of these latency issues. Consider social media image filters and location-based dining recommendations — these are app features that require low latency to deliver results at their highest level.
As aforementioned, cloud processing times can be slow, and ultimately, developers need near-zero latency in order for ML features to operate properly in their mobile apps. On-device machine learning paves the way for near-zero latency with its data processing abilities.
Smartphone manufacturers and the big tech players are catching up with this realization. Apple has been leading on this front, developing more advanced smartphone chips using its Bionic system, which has an integral Neural Engine that helps neural networks run directly on-device, with incredible speeds.
Apple also continues to iterate on Core ML, it’s machine learning platform for mobile developers; TensorFlow Lite has added support for GPUs; and Google continues to add pre-loaded features to its own ML platform, ML Kit. These technologies are among the ones mobile developers can use to develop applications capable of processing data at lightning speeds, eliminating latency and reducing errors.
This combination of accuracy and a seamless user experience is a primary consideration mobile developers need to take into account when creating ML-powered apps. And to guarantee this, developers will need to embrace on-device machine learning.
Increased Security and Privacy
Another huge benefit of edge computing that can’t be understated is how it increases the security and privacy of its users. Ensuring the protection and privacy of an app’s data is an integral part of a mobile developer’s job, especially given the need to meet the General Data Protection Regulations (GDPR), new privacy laws that certainly affect mobile development practices.
Because data doesn’t need to be sent to a server or the cloud for processing, cybercriminals have fewer opportunities to exploit any vulnerabilities in this data transference, thus preserving the sanctity of the data. This allows mobile developers to meet GDPR regulations on data security more easily.
On-device machine learning solutions also offer decentralization, much in the same way that blockchain does. In other words, it’s harder for hackers to take down a connected network of hidden devices through a DDoS attack when compared to the same attack targeting a centralized server. This technology could also prove to be useful for drones and law enforcement moving forward.
The aforementioned Apple smartphone chips are also helping to improve user security and privacy — for instance, they serve as the backbone of Face ID. This iPhone feature relies on an on-device neural net that gathers data on all the different ways its user’s face may look, serving as a more accurate and secure identification method.
This and future classes of AI-enabled hardware will pave the way for more secure smartphone experiences for users, offering mobile developers an additional layer of encryption to protect users’ data.
No Internet Connection Required
Beyond issues with latency, sending data to the cloud to be processed for inference requires an active Internet connection. Oftentimes, this works just fine in more developed parts of the world. But what about in areas of low connectivity? With on-device machine learning, neural networks live on the phones themselves. This allows developers to deploy the technology on any device at any given time, regardless of connectivity. Plus, it can democratize ML features, as users won’t need the Internet to connect to their applications.
Healthcare is one industry that can benefit greatly from on-device machine learning, as app developers are capable of creating medical tools that check vitals, or even ones that allow for remote robotic surgery, without any Internet connection. The technology could also help students who may need to access classroom material in a place without connection, such as a public transportation tunnel.
On-device machine learning will ultimately give mobile developers the tools to create applications that can benefit users from all areas of the world, regardless of what their connectivity situation looks like. And even without an Internet connection, given that newer smartphones are as powerful as they are, users won’t be plagued with latency issues when using an application in an offline environment.
Reduced Costs for Your Business
On-device machine learning is also designed to save you a fortune, as you won’t have to pay external providers to implement or maintain these solutions. As previously mentioned, you won’t need the cloud or the Internet for such solutions.
GPUs and AI-specific chips are the most expensive cloud services you can buy. Running models on-device means you don’t need to pay for these clusters, thanks to the increasingly sophisticated Neural Processing Units (NPUs) smartphones have these days.
Avoiding the heavy, data-processing nightmare between mobile and the cloud is a huge cost-saver for businesses that choose on-device machine learning solutions. Having this on-device inference also lowers bandwidth demands, ultimately saving a hefty sum in costs.
Mobile developers also save greatly on the development process, as they won’t have to build and maintain additional cloud infrastructure. Instead, they can achieve more with a smaller engineering team, thus allowing them to scale their development teams more efficiently.
There’s no question that the cloud has been a boon to data and computing in the 2010s, but the tech industry evolves at an exponential rate, and on-device machine learning may soon be the standard in mobile application and IoT development.
Thanks to its reduced latency, enhanced security, offline capabilities, and reduction in costs, it’s no wonder that all the major players in the industry are betting big on the technology, which will define how mobile developers approach app creation moving forward.
If you’re interested in learning more about mobile machine learning, how it works, and why it matters in in the overall mobile development landscape, here are a few additional resources to help get you started:
- Matthijs Holleman’s blog Machine, Think! has a bunch of great tutorials and other content on Core ML, Apple’s mobile ML framework.
- Awesome Mobile Machine Learning: An “awesome” list of resources covering all aspects of the intersection of mobile dev and machine learning.
- Artificial Intelligence at the Edge (video)
- And, of course, Heartbeat has a growing library of resources on the intersection of mobile development and machine learning.