Technology is advancing each day and is not failing to amuse us. Be it AI chatbots talking to us and having a real conversation or playing the mobile games of mobile phones in the real world with augmented reality.
The technology is evolving itself at such a drastic rate that machine learning is now no more an alien thing to us. Machine learning app development is gaining momentum recently because of technological advancement and user’s needs.
Machine learning apps are not new in the mobile app industry but still attract users because of ease of usability and smart results that improve performance. In this blog, we will talk about what is the right approach to do machine learning app development if you are new to the concept.
Often when people think about machine learning, they think of forums, lots of numbers, logistics, calculus figures in the air, and what not. Well, it is very important to simplify the meaning of the concept.
It is the science or process where computers are programmed to learn and adapt things like a human to deliver some kind of result. In the words of AI company Emerj,
“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in an autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
The Machine learning algorithms used in machine learning app development could be bifurcated in 3 ways-
In the supervised learning algorithm, the software learns the data on basis of the input and output of a particular instance. Keeping that as a reference, it learns the counter results.
In this, the machine learns the answers and the results from the untagged data. It uses algorithms from unlabeled data. It helps in finding results to hidden patterns without human intervention.
This type of learning is done when intelligent agents learn how to behave in a particular environment based on reward and award patterns. The machine takes a decision in a particular situation and receives an award or punishment accordingly.
It is no wonder that your Sci-fi movies that are the fiction of today will be the reality of tomorrow. The machines and apps are getting smart enough with data learning that they will be able to act and react like a human mind. There are multiple machine learning apps that will be very common to see in times to come.
If you want to do a startup in 2021 or times to come, you can think for machine learning app development for the following-
And these are only a few of them. The machine learning apps will keep expanding with the passing time.
Machine learning is a very strategic and smart software that needs to be integrated into your app in smart ways. Listed below are major models that can be introduced while doing machine learning app development.
1.Use Pre-Built Models
This is a shortcut way of machine learning app development. There are already made models and you need to replicate them and then introduce them in your mobile app. In this process of machine learning app development, you can save a lot of time by skipping finding data, training the machines, and accuracy testing as it is already done.
However, what is important is that you go for the models that are rich in data and have good results and response rates. As you can not lay the foundation of your machine learning app on a weaker model. Else it would collapse.
You can rely on the following models for your machine learning app development-
However, it is recommended that you hire machine learning app developers even in the pre-built modes. As they know what to fix and optimize to provide you best results and good functionality.
There are different models for different activities, like image segmentation, face Id, biometric, etc. So you should ask the experts before implementing them of their own.
2.Convert Between Model Formats
It might happen that you hire developers to do machine learning app development for the android platform but they have no knowledge of how to do it over iOS, then it is of no big use. Unless you are planning to launch your app on android only.
You need to hire developers that are skilled enough to transfer different learning models on multiple platforms to perform the best results.
There are different tools like Keras and Caff that don’t export models directly.
So you need to have a step-by-step approach where each model that is used in the machine learning module is converted from one format to another in machine learning app development. So that it shows similar results on different platforms. Keep checking out good converters that allow the model conversion is a tip that will become handy in your development process.
3.Focus on Native Development
The cross-platform apps may appeal to the aggregator at first but they tend to outsee the long-term effects that come later in the stage. Sice machine learning is all about providing efficient services based on its search from the data set. The cross-platform transfer may weaken your machine learning process. If you want to develop an app that should be known for good user experience and accurate results, I would suggest you go and build the app from scratch. You can seek consultation from experts that can guide you on how to start with machine learning app development.
Here are the following steps that you can go with if you are planning for machine learning app development from the beginning.
You first have to ideate and find out what your app will offer. What your machine learning app will study and observe and on that basis what results in it will be shown. It could be based on weather forecasts or predictions based on data sets that you will provide.
2.Collect and clean the data
For your app to learn and work on data, you need the data. You need to collect the relevant data. Once you collect the data you need to filter it out and then store the database that is useful for machines to learn, adapt and perform the desired results.
3.Prepare data for Machine Learning App
Collecting the relevant data and then filtering it out is not enough. Once you have collected the data you have to ask the developers to convert it into the language in which machines can learn and understand. This could be done by building a data pipeline on machine learning needs.
It refers to the technique where two or more existing features are combined so that machine gives better and more accurate results. It is needed because it is not necessary that raw data collected may always provide the related results.
5.Training a model
In this, as the name suggests, we need to train the machine for learning. But before doing that, the data is divided into two portions. One is used for training and another one is used for evaluation. This helps in monitoring how good is the machine reacting to the data.
6.Evaluating and improving model accuracy
Under this, the part of machine learning app development, the machines are put to test to know their performance. Based on the learnings they got so far through the data consumption and algorithms the evaluation is done.
On basis of their performance, they are awarded or punished. This means for example if performing well, that part or functionality may go under perfect. And other that is not to the mark is put under the tab of under-fitting or over-fitting and then send to reassessment.
7.Serving with a model in production
After the model is good to go, it is run on the unseen data as well, and then the model has to be kept in supervision how it reacts when it comes in contact with real-world data and impacts the business decisions with its forecasting and predictions.
The machine learning apps themselves are like a wonder. It is like creating an artificial brain. And therefore the cost could be very high. But it all comes to worth when it shows amazing results and helps you to reduce the cost of your business with the right predictions. The costing could be varied on different parameters like
It means how you want to introduce machine learning app development in your app. In this, I am highlighting the approaches mentioned above through which you can make machine learning apps.
In this, the machine learning apps could learn data from different types and could deal in different domains. Like it could be related to gathering and learning from the image, others from the text, some from figures, or others from the human response. So which nature of your app is also contributing to costing.
It is obvious that more the time will be taken for your app, the more will be time used to collect data, train the machines, evaluate them and then come to a results reaction. So this will add top cost. Also, another thing is, that if you want to speed up the process of your machine learning app development, more resources will be employed. This will be a cost contributor. It actually depends on the situation.
The more is the data, the more will be the cost. It requires more labor when you have a big database. As this data collection, then cleaning and sorting it out takes a lot of time for the developer which will add to the cost.
You can expect your machine learning app if built from scratch will cost around 40,000 US Dollars to 150,000 US Dollars. However, I recommend consulting experts for the right estimates.
It is quite interesting to make, machines mimic the brain of a human to act in an according to the way so that it could daft the results that could impact the business. You can make machine learning either for yourself or the third party because the world will be taken over by Ai in the times to come. And there is no harm to stay ahead in the race by keeping up with the technology.
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An avid reader and non-traditional thinker. Aayushi started her career at age of 22 which allowed her to write about the latest trends and technology that are new in the market. Identifying herself as a mobile geek, she finds pleasure in exploring apps and trends in the Mobile industry. Commerce graduate and Masters in Finance, she is well versed with aspects of what it takes for any brand to mark its position in the market. Being a certified Content marketer and influencer from HubSpot; she is familiar with brand positioning and the latest trends running in the IT and Digital world.
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