How Machine Learnings is Shaping the Future of Mobile App Development
The revolutionary developments in the field of machine learning have had a huge impact on mobile platforms. Today, mobile phone functionality has become far more sophisticated which allows for more flexibility in mobile app development. Developers are now able to produce mobile applications that function seamlessly between devices.
Years from now when the history of mobile app development is told, the role of Machine Learning will be considered a keystone in the development of the mobile experience.
Let us look at 3 key roles Machine Learning (ML) will play in creating the next wave of mobile apps:
1. Lower Latency
Mobile app development services are usually provided by developers with a goal of the end product having a low latency. Regardless of how strong the features are or how renowned the brand is, high latency can ruin the app experience completely.
Some of the android devices faced latency issues with a number of apps which led to a disorienting and lackluster viewer experience. Likewise, a social media app with high latency will drive users to better functioning platforms.
As developers were increasingly getting frustrated with these complexities, ML development served as a viable tool. Today, machine learning is performed on-device mainly because of these latency issues.
Take social media image filters and location-based dining recommendations for example. These app features demand low latency and deliver excellent results.
Cloud processing times can be slow and developers need zero latency in order for Machine Learning features to operate smoothly on their mobile apps.
Therefore, on-device machine learning is used to achieve near-zero latency with superior data processing abilities.
2. Improved Security
Another benefit of Machine Learning is how it has helped scale up levels of security and privacy. Mobile developers are cautious about ensuring the protection and privacy of their app’s data. If your app operates in the EU, you must be mindful and comply with the General Data Protection Regulations (GDPR).
Now that the data is not sent to a server or cloud for processing, the potential for cybercrime decreases dramatically.
Taking security enhancements one step forward, the on-device ML development offers complete decentralization of data storage. This makes it even harder for data exploiters to attack a connected network of discrete devices through a DDoS attack as compared to attacking a centralized server. With drones becoming more ubiquitous, this technology will bolster their efficacy significantly.
3. No Internet Connection Requirement
An active internet connection is required for sending data to the cloud for processing. Usually, this works well in more developed parts of the world with strong internet infrastructures.
However, what about areas with low internet connectivity? With on-device machine learning, neural networks can remain active on the phones themselves. With this new tool, developers can implement the technology on any device, at any time, and not rely as heavily on their user’s internet connection.