How to Integrate Machine Learning in Mobile App Development?
Summary: The global ML market was valued at USD 1.41 Billion in 2017. It is now expected to grow up to USD 8.81 Billion by 2022 at a CAGR of 44.1%. The major reasons behind this ample market growth are its prowess in data generation and data analysis which further lead to innovative decision-making by machines.
Machine Learning is basically the learning of machines that helps them become smart. It is the base technology that enables Netflix to know and analyze your usage preference, content choices, shows that you might like, amongst others. It is also behind Google Photos when it automatically analyzes pictures and tags the person(s) within it.
What exactly is ‘Machine Learning’?
Machine Learning is basically a subset technology of Artificial Intelligence. As AI makes a machine smart, machine learning is the algorithms and data inputs that ‘train’ the machine to become smart. It basically includes technology concepts and developments that enable a machine to learn from the data sets that are given to it and get smarter in experience and time.
There are basically 3 types of machine learning algorithms:
1. Supervised Learning with Labeled Data
Herein the algorithms are fed with we-organized data, wherein the inputs and results are extrinsically labelled and marked. These algorithms typically require a lot of data to analyze and understand the requisite patterns. Supervised Learning basically helps the machine to understand and analyze the relationship between inputs and outputs amongst these data sets. Linear regression for regression problems is solved through supervised learning algorithms.
2. Unsupervised Learning with Unlabelled Data
In these algorithms, there are no set patterns of data given to the machine. The data is neither labeled, nor are the machines told about their job’s endpoints. Herein, the system first sorts through this data and then tries to analyze a pattern within it. Any new data input to the machine is then put by the algorithms of the paradigm of the pattern analyzed and look for related results. The goal of unsupervised learning is to find the underlying structure of a dataset and then group this collected data according to similarities and dissimilarities to outline a pattern that represents the dataset in a compressed format. Customer segmentation algorithms and analyzing DNA pattern algorithms are some known examples of Unsupervised Learning algorithms.
3. Reinforced learning with rewards
Reinforced learning is basically a type of unsupervised learning as it does not require any labelled inputs or outputs within the data. The job of these algorithms is basically to find a balance between sorting through unknown data and exploitation of current knowledge. The correct results are then again fed to the machine with an element of the reward to let the machine understand what it requires to do. Deep Reinforcement Learning has found application with autonomous driving technology developments.
Integrating ML within a Mobile Application
The basic concept underlying Machine Learning is that it enables inputting a lot of ‘data’ that helps to ‘train’ the machine. From training, the machine tends to ‘learn’ and analyze patterns and gain ‘experience’ from the same. This experience then helps the machine to make a decision or find a solution. The machine may also ‘adapt’ to environmental changes or stimulations in this process. Let us now understand the technical steps involved to create this process:
- Choosing the apt platform: The first step for integrating ML within your app is to analyze the platform you wish to utilize for its development and then look for hiring a developer that could do the task well.
- For a novice developer, Google offers its ML kit which is a cross-platform suite of ML tools that includes API’s for various functionalities. It includes six basic APIs with pre-trained models for image labeling, text recognition (OCR), landmark identification, face detection, barcode scanning, and smart reply; making it very easy to use.
- TensorFlowis another ML integration platform by Google that offers highly advanced APIs to the developers for inclusion. It is basically based upon the Keras API standards that enable fast prototyping, state-of-the-art research, and production through Apis.
- CoreMLis Apple’s kit to integrate Machine learning within its apps. It enables the easy creation of ML models (a necessity for CoreML) using the CreatML app. Core ML only allows predictions on the basis of models, but it does not enable the training of these models.
- PyTorch is an ML integration toolkit developed by Facebook that uses dynamic computational graphs for decision making.
- Organizing ‘Models’: The most important step of integrating ML within a mobile app is developing the right ‘model’ on which the inputs of data and algorithms to sort through them will be based upon. Since these models tend to perform iterative tasks and decisions on that basis, they are pretty complex to develop.
- Deploying the Models: Machine Learning models are only effective when developed with huge datasets. But, huge datasets require huge spaces for storage and then huge computational powers for data sorting and analysis, which are mostly not available with small developers. Thus, most of the time developers hire cloud services for these tasks. Amazon’s SageMaker, Google’s ML Engine, and Microsoft’s Azure AI are some of the largest cloud platforms for Machine Learning today.
ML is here to stay
Businesses have been in awe of this technology, as it tends to develop ‘robot-like’ machines that would take human-like decisions, but with much more accuracy. But remember, that no technology is flawless. Machine Learning too includes flaws that developers and technologists are trying to resolve. The technology, on the whole, has proven its worth in gold and is for sure here to stay. After all the world loves ‘smart’, be it machines or people.