Know the step to develop effective wireframes using machine learning tools
In 2010, the term Big Data became a trending buzzword and that was the time when other emerging technologies like Artificial Intelligence and Machine Learning caught on speed. From big data to deep learning, many new technologies made their way into different industry vertical which included the field of mobile app development as well.
So in this article, we will be shedding some light on the various roles of machine learning, one of them being developing wireframes for mobile applications. The thing about machine learning applications is that they are virtually endless and that leaves a great scope of use cases to be explored.
According to a study released by SalesForce, it was seen that 57% of the total customers are willing to share their data with brands and companies that plan to use it to make their user experience more pleasant.
Now let's straight move on to the part where we will be sharing everything you need to know about effectively using the machine learning tools for developing the mobile app wireframes.
Today, smartphone plays a vital role for tech-savvy developers as well as budding entrepreneurs from all around the world who working hard to build a cutting-edge mobile app to succeed in the market. But there are just a few professionals when it comes to leveraging contemporary technologies like ML i.e. machine learning tools.
Below are the steps that are required to develop wireframes using ML in mobile development and other technologies like big data:
In the case of machine learning app development, it is always better to start by pre-planning about the procedure once the R&D is done. This stage is crucial and cannot be taken lightly so make sure you perform an in-depth research and emphasize on significant conceptualizing before moving on with the next step.
Here are some questions that you need to ask yourself as an app developer:
If you answer these questions clearly then it will not only simplify the entire process but also clarify the upcoming steps of the procedure of using machine learning in mobile apps.
The next step is to perform a rough or mental prototyping for your application which a mobile app developer can initiate after the discovery stage is completed. In this phase, a step by step approach is taken into consideration for building the scope for the app development project.
The above images shows the basic example of the prototyping stages of a mobile application.
This is the part where you will be required to perform an entire psychological prototyping of your application so that you can visualize the primary idea on which your mobile app will be working. The developer can do so in the form of various whiteboard sketches to make the process easy to understand.
The second stage is used to find the usability issues that can be faced by the app users and to overcome this you can go ahead and gather the feedback of the app testing team. Once the loopholes are exposed, the developer can work on eliminating them and then checking the app again for any issues.
One mistake that many developers make is thinking that having complete comprehension of the visuals is sufficient when it's clearly not! So if you are someone who doesn’t know the coding or ML algorithms for developing wireframes then you need to understand the technical possibilities of your mobile apps.
Here, you also need to ensure that the back-end machine learning app development can also support your app’s functionality efficiently as well as effectively.
At this point, you need to make sure that the notion of your mobile application is completely possible and for that you have to obtain access to its public data as well and this can only be done via the APIs. Just know the exact stage that you are developing your app for the platform you are choosing, for example, iOS or Android.
Once you have designed and developed the app, the next thing you need to do when it comes to using machine learning in mobile apps is to test it. Go ahead and figure out the technical possibility then make a prototype for your application. This will also help in developing a basic framework for the final outcome of your app that will be visible to the end-users.
Apart from this, deploy your app ensure that you have tested your apps a couple of times and resolved the issues that were surfaced during this stage to make the app more appealing for the target audience.
It is very essential that you continuously keep collecting data from the end-users and optimize the ML algorithms to upgrade your mobile application up to its full efficiency. Here are some of the mistakes that the app developers need to stay clear from:
To avoid these things, the developer should focus on collecting data from customers engaging with your apps. This will make it easier to understand the users' behavior and improve the UX (User Experience) of your system as well.
Also, keep in mind that you can get better insights by researching on your customer behavior with the help of demographics and analytics. Be patient because optimizing your app can take some time with some deep understanding of machine learning but the payoffs will also be huge.
So these are steps that you need to know to develop wireframes for mobile applications while using machine learning. The ML algorithms and machine learning tools are not just limited to this use as there are many other machine learning use that we will be covering in MobileAppDaily's upcoming articles so stay tuned.
Our expert suggestion would be to keep yourself updated with the advancements made in emerging technologies like artificial intelligence, machine learning and big data. And if you like reading such informative articles on trending topics of the mobile app industry then make sure you click on that 'Subscribe' button to stay notified.
Twinkle is an experienced business and marketing consultant of the mobile app industry. She advocates perfect branding to the latest tech releases. She is passionate about writing well-researched reports to help the app owners and the mobile app industry audience. Also, she has a vibrant touch that goes well in her writing as well.
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