A Guide to Machine Learning App Development

Machine learning has smoothly weaved into our daily lives, and we do not even realize it. From asking Google for directions to using Siri, we have slowly become entirely dependent on machine learning.

So what exactly is machine learning?

In simple terms, it is a subfield of artificial intelligence that is made to learn and adopt the capability of machines to imitate intelligent human behavior by using certain algorithms and statistics to make inferences.

Many apps are circulating the market currently that have made the lives of humans extremely easy. If you go looking, any mobile app developer who is an expert in the field can provide you with the service accordingly.

But can you develop a machine learning app? 

It may be a bit difficult, but it is not impossible. Here is a guide that can help you understand machine learning app developments:

Know the Problem

While this may be the most obvious step in the machine learning app development process, it is often missed.

Artificial intelligence has quite some hype in the digital world today. It is easy to get swept up in all the rage that artificial intelligence is creating. But it is crucial here to think about the logic behind the development of machine learning apps.

Think about the needs and specifications of your target audience and whether you will create convenience for them with the machine app development or not.

Ask yourself:

• Is it absolutely important for my business to make this machine-learning app?
• Can we forego ML and go for anything else?
• How can we add value for our customers with this?

If any of the questions yield relevant answers, then you should opt for machine learning app development. Here, your main focus should be providing a good feature for your customers that they can facilitate from.

You should also consider the machine learning guides provided by Google, Apple, and Microsoft. The below-mentioned questions are important to ask 

• How can we warrant a successful result for the customers?
• Does our ML have the potential to evolve gradually?
• Do we have any alternatives if our ML fails?
• Are we facilitating our customers to provide feedback?
• Is our data enough to train a successful model?

By answering all the above questions, you will be able to come to a reliable conclusion regarding your decision to use machine learning app development. You should be equipped with enough alternatives to ensure that you are not suddenly shocked if anything goes sideways.

Hire the Right Professionals

Machine learning is not just a task of a single person. It will take a team of competent professionals to showcase their skills and work expertly to build a machine-learning mobile application.

In addition to application developers, you will also need designers, full-stack backend developers, QA engineers, data scientists, and analysts to ensure the smooth flow of the whole process.

You will need special developers and analysts who specifically deal with data and create ML apps and models.

Determine the Structure of the Application

Before the advent of ML, developers always had to choose an external server for enabling AI features in different applications as needed. The main objective here is the presence of a robust ML infrastructure that can run different data analyses combined with an app that retrieves important output from the server.

Now, the integration of ML has become quite easy for you to use. You can effortlessly decide the position of ML in your app. This can be according to your precise needs and what is feasible for you.

Some of the options where you can keep your ML can be:

• Custom libraries
• On-device SDK
• Cloud hosting with APIs
• Hybrid approach

Choosing the right place from the options mentioned above can be hard. The easiest option here is to keep the ML within the app that you built. It is safe and can be integrated simply.

Choose a Premade Template for Machine Learning App Development

When you plan on machine learning app development, you have two options here. Either build from scratch or choose a template off the shelf and start from there.

Here, think about the possibilities you have if you develop ML from scratch. The development of machine learning from scratch will offer more flexibility. You can choose and pick any tech stack, feature, and AI tools that you want and include them.

On the other hand, if you go for a template ML, they would take a lot of burden off your shoulders. Some specific features will already be available, and you will not need to go through the trouble of adding everything.

The tech stack options include:

• AI/ML Stack: this includes a good amount of ML frameworks that allow you to expertly build different machine learning models. Some solutions are MXNet, TensorFlow, Keras, Caffe, etc
• Programming Language: the most common language for machine learning app development is Python. This language exceptionally works with any AI/ML library. This provides an ease to the users.

The off-the-shelf options include:

• Mobile app hosting ML services: these are dominated by Google and Apple as they are the owners of Android and iOS, respectively.
• Cloud-hosting ML services: the notable names here are IBM, Google, Microsoft, etc. All these companies expertly use AutoML, taking full advantage of the service.