10 min read

Last year, at Google I/O, Sundar Pichai, the CEO of Google, said:

“We are moving from a mobile-first world to an AI-first world”

Is it only applicable to Google? Not really.

In the recent past, we have seen several advancements in Artificial Intelligence and in parallel a plethora of intelligent apps coming into the market. These advancements are enabling developers to take their apps to the next level by integrating recommendation service, image recognition, speech recognition, voice translation, and many more cool capabilities. Artificial Intelligence is becoming a potent tool for mobile developers to experiment and innovate.

The Artificial Intelligence components that are integral to mobile experiences, such as voice-based assistants and location-based services, increasingly require mobile developers to have a basic understanding of Artificial Intelligence to be effective. Of course, you don’t have to be Artificial Intelligence experts to include intelligent components in your app. But, you should definitely understand something about what you’re building into your app and why. After all AI in mobile is not just limited to calling an API, isn’t it?

There’s more to it and in this article we will explore how Artificial Intelligence will shape the mobile developer role in the immediate future.

Read also: AI on mobile: How AI is taking over the mobile devices marketspace

What is changing in the mobile developer role?

Focus shifting to data

With Artificial Intelligence becoming more and more accessible, intelligent apps are becoming the new norm for businesses. Artificial Intelligence strengthens the relationship between brands and customers, inspiring developers to build smart apps that increase user retention.

This also means that developers have to direct their focus to data. They have to understand things like how the data will be collected? How will the data be fed to machines and how often will data input be needed? When nearly 1 in 4 people abandon an app after its first use, as a mobile app developer, you need to rethink how you drive in-app personalization and engagement.

Explore “humanized” way of user-app interaction

With so many chatbots such as Siri and Google Assistant coming into the market, we can see that “humanizing” the interaction between the user and the app is becoming mainstream. “Humanizing” is the process where the app becomes relatable to the user, and the more effective it is conducted, the more the end user will interact with the app.

Users now want easy navigation and searching system and Artificial Intelligence fits perfectly in the scenario. The advances in technologies like text-to-speech, speech-to-text, Natural Language Processing, and cloud services, in general, have contributed to the mass adoption of these types of interfaces.

Companies are increasingly expecting mobile developers to be comfortable working with AI functionalities

Artificial Intelligence is the future. Companies are now expecting their mobile developers to know how to handle the huge amount of data generated every day and how to use it. Here’s is an example of what Google wants their engineers to do:

“We’re looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day.”

This open-ended requirement list shows that it is the right time to learn and embrace Artificial Intelligence as soon as possible.

What skills do you need to build intelligent apps?

Ideally, data scientists are the ones who conceptualize mathematical models and machine learning engineers are the ones who translate it into the code and train the model. But, when you are working in a resource-tight environment, for example in a start-up, you will be responsible for doing the end-to-end job. It is not as scary as it sounds, because you have several resources to get started with!

Taking your first steps with machine learning as a service

Learning anything starts with motivating yourself. Directly diving into the maths and coding part of machine learning might exhaust and bore you. That’s why it’s a good idea to know what the end goal of your entire learning process is going to be and what types of solutions are possible using machine learning. There are many products available that you can try to quickly get started such as Google Cloud AutoML (Beta), Firebase MLKit (Beta), and Fritz Mobile SDK, among others.

Read also: Machine Learning as a Service (MLaaS): How Google Cloud Platform, Microsoft Azure, and AWS are democratizing Artificial Intelligence

Getting your hands dirty

After getting a “warm-up” the next step will involve creating and training your own model. This is where you’ll be introduced to TensorFlow Lite, which is going to be your best friend throughout your journey as a machine learning mobile developer.

There are many other machine learning tools coming into the market that you can make use of. These tools make building AI in mobile easier. For instance, you can use Dialogflow, a Natural Language Understanding (NLU) platform that makes it easy for developers to design and integrate conversational user interfaces into mobile apps, web applications, devices, and bots. You can then integrate it on Alexa, Cortana, Facebook Messenger, and other platforms your users are on.

Read also: 7 Artificial Intelligence tools mobile developers need to know

For practicing you can leverage an amazing codelab by Google, TensorFlow For Poets. It guides you through creating and training a custom image classification model. Through this codelab you will learn the basics of data collection, model optimization, and other key components involved in creating your own model. The codelab is divided into two parts. The first part covers creating and training the model, and the second part is focused on TensorFlow Lite which is the mobile version of TensorFlow that allows you to run the same model on a mobile device.

Mathematics is the foundation of machine learning

Love it or hate it, machine learning and Artificial Intelligence are built on mathematical principles like calculus, linear algebra, probability, statistics, and optimization. You need to learn some essential foundational concepts and the notation used to express them.

There are many reasons why learning mathematics for machine learning is important. It will help you in the process of selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features. Maths is needed when choosing parameter settings and validation strategies, identifying underfitting and overfitting by understanding the bias-variance tradeoff.

Read also: Bias-Variance tradeoff: How to choose between bias and variance for your machine learning model [Tutorial]

Read also: What is Statistical Analysis and why does it matter?

What are the key aspects of Artificial Intelligence for mobile to keep in mind?

Understanding the problem

Your number one priority should be the user problem you are trying to solve. Instead of randomly integrating a machine learning model into an application, developers should understand how the model applies to the particular application or use case.

This is important because you might end up building a great machine learning model with excellent accuracy rate, but if it does not solve any problem, it will end up being redundant. You must also understand that while there are many business problems which require machine learning approaches, not all of them do. Most business problems can be solved through simple analytics or a baseline approach.

Data is your best friend

Machine learning is dependent on data; the data that you use, and how you use it, will define the success of your machine learning model. You can also make use of thousands of open source datasets available online. Google recently launched a tool for dataset search named, Google Dataset Search which will make it easier for you to search the right dataset for your problem.

Typically, there’s no shortage of data; however, the abundant existence of data does not mean that the data is clean, reliable, or can be used as intended. Data cleanliness is a huge issue. For example, a typical company will have multiple customer records for a single individual, all of which differ slightly. If the data isn’t clean, it isn’t reliable. The bottom line is, it’s a bad practice to just grabbing the data and using it without considering its origin.

Read also: Best Machine Learning Datasets for beginners

Decide which model to choose

A machine learning algorithm is trained and the artifact that it creates after the training process is called the machine learning model. An ML model is used to find patterns in data without the developer having to explicitly program those patterns. We cannot look through such a huge amount of data and understand the patterns. Think of the model as your helper who will look through all those terabytes of data and extract knowledge and insights from the data.

You have two choices here: either you can create your own model or use a pre-built model. While there are several pre-built models available, your business-specific use cases may require specialized models to yield the desired results. These off-the-shelf model may also need some fine-tuning or modification to deliver the value the app is intended to provide.

Read also: 10 machine learning algorithms every engineer needs to know

Thinking about resource utilization is important

Artificial Intelligence-powered apps or apps, in general, should be developed with resource utilization in mind. Though companies are working towards improving mobile hardware, currently, it is not the same as what we can accomplish with GPU clusters in the cloud. Therefore, developers need to consider how the models they intend to use would affect resources including battery power and memory usage.

In terms of computational resources, inferencing or making predictions is less costly than training. Inferencing on the device means that the models need to be loaded into RAM, which also requires significant computational time on the GPU or CPU. In scenarios that involve continuous inferencing, such as audio and image data which can chew up bandwidth quickly, on-device inferencing is a good choice.

Learning never stops

Maintenance is important, and to do that you need to establish a feedback loop and have a process and culture of continuous evaluation and improvement. A change in consumer behavior or a market trend can make a negative impact on the model. Eventually, something will break or no longer work as intended, which is another reason why developers need to understand the basics of what it is they’re adding to an app. You need to have some knowledge of how the Artificial Intelligence component that you just put together is working or how it could be made to run faster.

Wrapping up

Before falling for the Artificial Intelligence and machine learning hype, it’s important to understand and analyze the problem you are trying to solve. You should examine whether applying machine learning can improve the quality of the service, and decide if this improvement justifies the effort of deploying a machine learning model.

If you just want a simple API endpoint and don’t want to dedicate much time in deploying a model, cloud-based web services are the best option for you. Tools like ML Kit for Firebase looks promising and seems like a good choice for startups or developers just starting out. TensorFlow Lite and Core ML are good options if you have mobile developers on your team or if you’re willing to get your hands a little dirty.

Artificial Intelligence is influencing the app development process by providing us a data-driven approach for solving user problems. It wouldn’t be surprising if in the near future Artificial Intelligence becomes a forerunning factor for app developers in their expertise and creativity.

Read Next

10 useful Google Cloud Artificial Intelligence services for your next machine learning project [Tutorial]

How Artificial Intelligence is going to transform the Data Center

How Serverless computing is making Artificial Intelligence development easier


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