It’s not that AI and ML are new and have never been implemented before. It is present on our mobile applications. Some typical examples are-
Once you have an idea about AI and its benefits, you’ll know the point at which you can apply it to your mobile app and if it will help you in your business growth. Let’s get into how to create an app using AI/ML for your business.
To create a mobile app using AI/ML, you need a well-thought process so as to optimize time, cost, effort, and resources. Since it is an AI-based app, the costs and the time required for app completion definitely go up. But proper planning at each stage will ensure app completion within your budget and time. Let’s see the basic process pipeline to ensure the best results.
Just because the trend is AI/ML, you cannot be blind and incorporate advanced features in your app. First, ask if AI/ML is essential? How will it improve user experiences? Is it going to solve a challenge? Is the investment worth it? If including AI/Deep Learning in your mobile app makes your app stand out from the competition, and if it offers value to the customers, it is worth considering. Identify the problem and how AI will help it.
One thing to be taken care of is that the mobile app company should be well-versed with AI/ML technologies. Before selecting one, ensure that they check the right boxes.
Once you’re settled with the AI and Machine Learning services, it’s time to get into the most crucial task, app designing. Your vendor will decide the required tech stack based on your app’s needs. Make sure you convey your requirements clearly and well in advance for them to pick the correct framework that supports your app needs. Let’s discuss some popular AI/ML/Deep Learning software tools needed to develop a mobile application. The choice of AI technology depends completely on the company’s business requirements.
AI/ML tools, libraries, APIs, and platforms to create AI-based mobile apps
An artificial intelligence-based mobile application is hard to build and requires proficient AI/ML-ETL development services. Typically, Python, Java, and C++ are used as programming languages for AI-based applications. But now, third-party AIaaS (AI as a Service) products are available that make designing AI-based mobile apps easy and affordable. The most popular products are-
The Microsoft Cognitive Toolkit (CNTK) is a deep learning open-source library for machine learning and deep learning. Created as a training algorithm for machines to learn like humans, it can be used to create various ML models. It makes it easy for neural networks to process large amounts of unstructured data. Developers can select the metrics, networks, and algorithms and customize them as per the app requirements.
Amazon has the AWS Deep Learning Containers (DL Containers) that support ML frameworks and make it easy to deploy and optimize the ML environments. The DL Containers have pre-packaged Docker images to deploy MI frameworks such as TensorFlow, PyTorch, and Apache MXNext in minutes. The developers can add their own libraries and tools on top of these images for a higher degree of data processing, compliance, and monitoring.
TensorFlow is an open-source platform (library) consisting of an ecosystem of tools, libraries, and community resources that helps developers build and deploy MI-powered applications. Keras, an open-source library written in Python has been integrated with TensorFlow. It is a product of Google developed for deep learning applications. This library provides a high-level API and has integration with Java and R. It supports GPUs (Graphical Processing Units) and CPUs (Central Processing Units). It offers faster compilation than other deep learning libraries.
PyTorch is an optimized library effective in creating Deep Learning applications. It is based on Python and Torch and is preferred over TensorFlow since it uses dynamic computational graphs and completely uses Python language. It uses tensor computation to make operations faster. A tensor is a container that can hold data in multiple dimensions. PyTorch also has the Automatic Differentiation feature for creating and training deep neural networks.
Other popular AI/ML frameworks are H2O, Petuum, Polyaxon, DataRobot, NeuralDesigner, Apple Core ML, Caffe2, and PredictionIO. To start building your AI-based apps, you need powerful SDKs and APIs. Third-party tools like Microsoft Face API, Google Vision API, and Apple’s SiriKit are some examples. The right AI platform for your app depends on the capabilities you want to incorporate into your app. Hire reliable AI and Machine Learning services and talk to their experts regarding your requirements. They will decide the most appropriate software needed to build an AI/ML mobile app.
A special word on data for AI-based mobile apps. Typically, data is the epicenter of AI/ML applications. It needs a large amount of relevant data. Ensure you have a good data mining and modeling technique in place. The better quality data you provide, the better outcome you can expect from the AI algorithms.
If you want to go for an MVP (Minimum Viable Product) or a full-scale app is your call. As per our suggestion, go for an MVP first. It saves you time and money. Test the app and look for errors and bugs before you go for the soft launch. Now, ask for feedback from the audience. Don’t rush to make a full launch. Sometimes, it takes 4-6 months for the app to be completely ready after the soft launch. A fully-functional app is always better than a bug-loaded app because if the user is not satisfied, it is difficult to bring them back.
Once you have a full launch, invest in updates and improvements. And ensure you provide good customer service.