Machine learning has revolutionized today’s world. Using programming techniques, machine learning provides applications the ability to provide a seamless experience to the users without being programmed or taught what to do.
No wonder why machine learning app development has become such an integral component of the tech industry. The following blog discusses some crucial steps to making a machine learning application.
Important Steps for Machine Learning App Development
According to the article by Topflight Apps, many companies are learning how to make a machine learning app because machine learning is embedded in many parts of our lives. From checking Google Maps for traffic to asking Siri common questions, machine learning has made technology more interactive.
However, just like any other complicated application, it takes a lot of effort to build a machine learning app. These applications have to be embedded with ML models, so they can benefit the users and provide a customized experience.
Accordingly, there goes a lot in the background when it comes to creating apps that use machine learning. However, there are some basic steps to creating a machine learning app:
Identify the Problem
This seems pretty obvious, but it is still one of the most important steps to build a machine learning app. Developers should ask what support they want to offer to their customers and if the task can be resolved without using machine learning. This is because some application ideas don’t require complicated architectures.
However, if the business needs cannot be resolved using common algorithms, then the company should consider how to build machine learning applications.
For instance, e-commerce websites rely on machine learning to provide product recommendations to their users. Social media platforms, like Facebook, also use machine learning algorithms to understand user activities, such as chats and likes, to recommend friends and pages.
Build the Model
Machine learning uses models that can perform certain tasks, recognize certain inputs, and make predictions. Companies can choose to create their models from scratch, but fortunately, companies can also select and customize an already built model.
For example, TensorFlow Hub has a lot of models from Google and other companies that can be run on Android.
Hire the Right Team
Undoubtedly, there is a lot of talent in the market. However, it is not always possible to get professional and talented individuals on board to work on a project. Companies require the support of not only app developers but also QA engineers, backend developers, designers, and data analysts, most importantly, to build the application.
Some companies struggle to find specialists in their local areas. Fortunately, remote hiring is now becoming a norm, and companies can look for talent offshore or beyond the borders. Plus, hiring abroad can also reduce the overhead costs of hiring an employee.
Think about the App’s Architecture
Developers also need to consider if their application interference will run on the device or a cloud service. Companies can rely on cloud hosting services, use on-device custom libraries, or even use a hybrid approach to use both options.
Obviously, there are pros and cons to each method. For instance, if the application uses cloud hosting services, then even users with slow and old phones can access the machine learning features.
Plus, there are many cloud-hosting ML services in the industry, such as Microsoft, AWS, IBM, and Google. Amazon Web services also offer cloud-hosting facilities through Auto-Gluon and Sagemaker.
On the other hand, when the applications are kept in mobiles, then machine learning offers almost zero latency.
Dedicated hardware, combined with CPUs and GPUs, can also make the user experience more seamless with faster performance. Most importantly, using this approach, companies can ensure privacy and data security. When it comes to mobile app hosting services, Google’s Android and Apple’s iOS are definitely the most famous.
Pick a Programming Language
Deal with Loads of Data
Since machine learning analyzes the activity of the user, it is important to remember that the app has to deal with a massive influx of information. Accordingly, the applications should come with proper technology to manage all the data in a streamlined manner.
For instance, CometML is a popular service that makes it easy to collaborate and manage the data on an ML app.
At the same time, there should be ways for the application to leverage the data. Plus, collecting loads of data is not enough to run a machine learning application. The data should be cleared, normalized, and segmented as well.
After all, data can be sourced from a variety of sources. Accordingly, it should be optimized according to the standard format, so it can be used. The application should have the capacity to manipulate the data, transform the data to the desired values, remove statistical errors, and make sense of the data.
Keep Evolving the Application
The technological industry is always changing and growing. However, companies that fail to change as well, according to the flux in the industry, stay behind. Take the example of Nokia mobile, which never embraced the changing technology and eventually was toppled down by its competitors like Apple.
In this same manner, machine learning applications cannot survive for long in the market if other apps are offering more to the customers. Make sure to leverage the technology available and keep an eye on new trends, so the application can be developed and upscaled further.
Machine learning app development has become a need today because users need web solutions to most of their problems, and common architecture sometimes cannot resolve their concerns.
Machine learning offers avenues to leverage technology and create seamless applications for users that can translate languages, show addresses, predict health conditions, or simply offer product recommendations.