How to Integrate Machine Learning in Mobile App Development
When a machine seems to understand the user and make a choice for them or make a decision on its own; it is a machine learning algorithm functioning in the background. Let us analyze how these can be included within a mobile app to facilitate these decision-making functionalities.
Mobile apps nowadays are part of everything from banking transactions to viewing web content to managing shoes and managing health. They seem to be managing everything and evolving further to undertake more complex and transforming roles.
It is touted that, sooner rather than later, smart refrigerators will on their own be able to analyze groceries of a family and order as to what is required. And, smart cities will be able to map citizens mindsets to even locate those with a violent or objectionable streak. Fingerprint authentication systems by several fintech app development companies employ Machine Learning and so do Google, Facebook and Snapchachat, amongst others.
But, if gadgets, clothes, homes and cities are becoming so smart, what exactly is making them smart? These are the Machine Learning Algorithms!
Machine Learning based algorithms draw intelligent patterns out of the scores of data collected (Big Data) and utilize them to make gadgets smarter by enabling them to learn through these algorithms. They tend to make systems smart as data increases and their patterns evolve to more complex levels.
Machine Learning in Mobile Apps
When you open an app like Flipkart, they tend to help you shop by providing you a variety of product searches based upon your ‘recent searches’ or things that you had ordered. As the apps herein analyze your usage pattern to provide you with choicest options; it is an ML algorithm at the base.
Even the extensive video content options by Netflix or the Tinder options (as per profile and location) of choicest partners are also based upon extensive Machine Learning algorithms that help these mobile apps look for options as per user data without outright user interference.
Let us try understanding the same in terms of Spotify, one of the leading music apps out there. Its ‘discover weekly’ section features a list of personalized songs in the industry. The inherent reason for its high quality is the culmination of 3 extensive Machine Learning algorithms to make things work.
The first algorithm is Collaborative Filtering that enables the app to provide its users with personalized recommendations. It compares multiple user-created playlists with the history of the songs they have been listening to. The ML algorithm then compares it with other users' playlists with similar choices and recommends them.
The second ML algorithm relates to Natural Language Processing and helps the software to 'reads' song lyrics, blog posts and discussions about trending musicians. Then, based on this information, the algorithm suggests trending music and its exponents for the users.
The third is the Audio model algorithm that analyzes data from raw audio tracks and suggests other songs with similar music.
The above example clearly states how a simple looking music app, inculcates machine learning algorithms to take user experience to a whole new level of personalization. ‘Watch videos’ recommendations in Facebook and image recognition in Snapchat also use similar algorithms.
How to Inculcate ML Algorithms Within your Mobile App
Machine Learning algorithms are eminently supported by both app platforms (Playstore and Apple store). There are various frameworks and technologies like blockchain technology that can support or help you either build ML algorithms from scratch (this could be time and money consuming) or include an already created algorithm after customizing it as per your requirements.
A few of them are listed below:
- Google has launched a complete user-friendly version of tools within its ML Kit. ML Kit inculcates several powerful ML technologies like Cloud Vision, TensorFlow, and other Android Neural Networks API’s that otherwise require extensive expertise for development and deployment individually. ML Kit combines these specialist ML technologies with pre-trained models for common mobile use utilities, including extracting text from an image, scanning a barcode, and identifying the contents of a photo, etc. As of now, it is the most easy to deploy framework for ML.
- Tensorflow is an open source library for ML algorithms created and fully supported by Google. In fact, TensorFlow Lite, an extension of TensorFlow; has been specifically designed as a stack for mobile app development. It enables mobile app developers to easily integrate machine learning algorithms within the functionalities of mobile apps on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. TensorFlow computations are expressed as stateful dataflow graphs.
- Caffe (Convolutional Architecture for fast feature embedding) is another widely used deep learning software, developed by University of California, Berkeley. As it is developed in C++, with a Python interface; it is one of the most preferred frameworks for mobile app developers worldwide.
- Microsoft Cognitive Toolkit is a free, easy-to-use, open-source framework that enables commercial development of mobile apps and deployment of deep learning algorithms and their training to make gadgets learn to perform functionalities like the human brain. It has enabled successful development of several systems like feed-forward neural network time series prediction systems, amongst others.
Torch, MXNet, Chenar and Keras are some of the other famed and preferred ML toolkits that can help machines autonomously take human-like decisions.
How to use These Frameworks to Deploy ML Algorithms
If your mobile apps require high engagement with users in terms of better search ability and decision making, you should be looking for the apt machine learning framework and employable algorithms. The following steps will help you get into place the ML algorithms you require:
- In order to get your ML algorithm, you would require data to analyze base patterns. So, your first step should be to set into place processes to collect training data.
- Once you have the data, you would require algorithms to transform the data into required images and patterns.
- Once you have patterns , you will have to categorize them as per their requirements and work scenarios.
- Remember, you would require to set in place systems to refresh the data supply necessary to retrain the model with the fresh data.
- Optimize the model for mobile devices, by developing an apt .tflite file.
- Embed .tflite file into the application
- Test and run the application to find the flaws and test the functionalities.
With all app stores and device models for offering and supporting ML inclusions, its deployment is no more a challenge for experienced app developers.
It May Seem Easy, But it is Not!
Creating and deploying mobile apps with Human-like decision making prowess is a complex task that requires tons of expertise and experience in the field. Later on, these complexities might as well add on to how much does it costs to maintain an app.
Though, technologies like Google ML kit, seem to make things easy; their deployment by a layman is still out of question. We’ve already come a long way and the future prospects for this technology are very promising to say the least!