Machine Learning

How Machine Learning Is Transforming Mobile App Development

The advent of mobile technologies and smartphones brought forth a major cultural shift in people’s lives. A similar

The advent of mobile technologies and smartphones brought forth a major cultural shift in people’s lives. A similar paradigm shift is now going on in mobile app development, due to an older technology making a majestic comeback – Machine Learning. Though ML’s been around for a while, its potential wasn’t, or rather couldn’t, be explored a couple of years back. The technology is quite advanced now, enough to enable mobile apps to not require explicit programming to perform certain tasks.

A Machine Learning algorithm allows the mobile app to collect and analyzes lots of data to come up with precise conclusions. The system learns automatically after detecting app usage aspects and improve the app’s experience while it performs.

Easier said than done.

To do all this, the app requires sophisticated ML algorithms integrated into it as well as access to sufficient amounts of data sets, enabling the system to analyze previous experiences so as to make better decisions in the future. In addition, the system will adapt to new data. The apps essentially become more intelligent in the process.

Key takeaways for ML enthusiasts and developers

If you think it’s not yet time to build a Machine Learning application for your business, you would be taking the words back soon. McKinsey’s reports indicate that the total funding of ML apps is over $6 billion worldwide. Many businesses around the globe have already started preparations to effectively utilize the technology to get ahead of competitors.

That said, here are a few things that ML enthusiasts should always keep in mind.

  • Utilize all the data available – When it comes to leveraging machine learning for app development, developers should take care not to use sub-sampling, and utilize every data they have access to. Feeding more data to the algorithm helps it deliver more accurate results and predictions.
  • ML method could spell success or doom – The project’s success often comes down to the ML method selected. Unless there are large amounts of data involved, it’s best to stick to simple ML models for better predictions.
  • Pay attention to the parameters – Because the parameters and the methods should be constantly monitored, it’d be great to have a qualified data scientist onboard.
  • Improper data collection can cause problems – The efficiency of ML depends on the data used to train it. Improper data collection including incorrect labels, fragmented, poorly featured etc. can impact the efficiency of the ML algorithm.
  • Take client business model into account – The client’s business model should be considered before building ML algorithms for their benefit.

Top ML Frameworks for Mobile App Developers

ML’s rising momentum also owes it to the many great ML frameworks available at present and the support from popular cloud services. With many technologies including the cloud providing good back, app developers can now build ‘smart’ apps with cognitive learning capabilities, on-device processing, and minimum lines of code.

Here are the top ML frameworks that developers use to build ML-powered intelligent apps.

Google Tensorflow

At the top of this list is Tensorflow, Google’s contribution to create innovative Deep Learning models. The tech behemoth is reported to have invested a lot in AI/ML technologies, and is now letting everyone get a taste of the tech with its framework.

Tensorflow is based on computational graph that comprises of multiple nodes in a network, and each node is basically an operation that executes either a simple or a complicated function. Many popular Google services use Tensorflow to provide users with an intuitive experience. The same can be done for any mobile app now with the framework.

Apple’s Core ML

Apple’s machine learning framework Core ML was launched in the WWDC 2017, and is one of the main reasons why many iOS apps today are capable of performing tasks that human eyes do. Text & barcode detection, object tracking, face tracking etc. are just a few of Core ML’s supported features. In addition to machine learning, the framework also offers NLP APIs that can understand text.

Microsoft Cognitive Services

With Google and Apple rolling out powerful frameworks for ML, Microsoft couldn’t stay far behind. The Microsoft Cognitive toolkit features an ML framework offering sophisticated Deep Learning algorithms that can be used for multiple purposes including app development. Popular Microsoft services like Skype, Cortana, and Xbox were developed with the Cognitive toolkit.

One of its biggest advantages is that it allows use of popular programming languages like C++, Python, and Brainscript to build Deep Learning models. These models can be used to empower apps for Windows and Linux platforms.

Caffe

Out of the frameworks mentioned above, Caffe is the most unique. It was originally developed by Berkeley AI Research at UC Berkeley. The framework underwent many further refinements from community contributors. That’s what makes it unique. Caffe is open source and licensed under BSD-2 Clause.

Caffe is widely used for image classification, recommender systems etc. owing to its Convolutional Neural Networks (CNNs). A pre-trained model of Caffe is also available, called Model Zoo which can perform many tasks. With Caffe, developers can build apps for Windows, Mac, and Linux platforms.

Conclusion

The tech world considers Machine Learning to be one of the major factors that displays a business’ competence in just a few years. The technology is also expected to contribute to the Internet of Thing’s much anticipated mainstream dominance.

As of now, businesses can gain great competitive advantages by leveraging ML’s potential via enterprise mobile apps, not to mention better decision-making. The technology is worthy of investment, however only a few developers are actually qualified enough to mold the technology to fit into specific business models. Then there is an obvious need for great amounts of data for ML to make a significant difference.

AoT is one of those companies that can harness the potential of ML for your business through robust and secure custom enterprise mobile apps. We have already earned competence in new-gen technologies like AI/ML and IoT, complementing our already reliable expertise in mobile application development. Send us your queries to understand what ‘smart apps’ from AoT can do for your business.

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