How to integrate Machine Learning into your mobile app?

Sudeep Srivastava
Published on Jan 30, 2018 in App Development
How to integrate Machine Learning into your mobile app?

Machine Learning (ML) is reshaping our lives. From 10 million self-driving cars by 2020 to self-tuned databases, automated surveillance systems, smart assistants like Siri and humanoids like Sophia, it is everywhere. ML is making the devices, gadgets and apps smarter like humans, empowering them to take decisions on their own and facilitate us with a better experience.

Here are the findings of a ServiceNow and Oxford Economics recent study based on their interaction with 500 CIOs across 25 industries from 11 different countries,

  • Out of 72% of CIOs surveyed who claimed to be leading the digitization efforts of their companies, 52% agreed that Machine Learning played a crucial role in this.
  • Nearly 90% of them said that more automation will enhance the speed and accuracy of the decisions.
  • In the coming three years, the number of businesses investing in Machine Learning will be doubled (about 64%).

Besides this, Allied Market Research has predicted that Machine Learning as a service market will reach $5,537 million in 2023 while growing at a CAGR of 39.0% from 2017-2023. All these significant numbers have prompted the startups and established brands to turn towards the Machine Learning. And many have even proved that investing in Machine Learning is the best thing to do at the current time.

Want some real examples? Here are some of the companies/apps harnessing the power of Machine Learning to skyrocket their profit:

Uber

Uber is using Machine Learning in different ways to provide an exceptional experience to both the drivers and riders. It employs ML tools for providing an estimated time of arrival and cost to the riders, offering real-time detailed information in the maps to the drivers so that they meet the ETA condition, facilitating the users with real-time location of the driver, etc. In addition to this, the company is also relying on Machine Learning for dealing with fraudulent behavior through practices like face detection and not accepting the stolen credit cards.

Likewise, Machine Learning is used in UberEATS for restaurant recommendations, providing a precise estimated time of food delivery and real-time traffic condition.

Gboard

Gboard uses the neural spatial model to determine the touch points on the screen for serving users with more accurate typing experience. Gboard uses ML tools to predict the next word by matching the currently typed word with the user typing history and most appropriate phrases in English or other languages added. Thus, helps in typing faster. Besides, it also facilitates the users with the feature of finding the right emoji by drawing a blueprint of the same.

How to integrate Machine Learning into your mobile app?

Oval Money

Oval Money, with the Machine Learning techniques and tools employed by Facebook and Instagram, helps the individuals to track their expenses and save money significantly. It analyzes your spending habits along with those of the wider user community to assist you in making smarter savings decisions. It understands your behavior and provides a flexible saving process through which you can save efficiently and meet your financial goals. In other words, it plans how to readjust your spending and saving activities to save more money. Apart from these, it also recognizes duplicate payments and informs you about the same.

Netflix

Netflix is employing Machine Learning for understanding user behavior and offering personalized TV and movies suggestions. In fact, 80% of TV shows watched on Netflix are suggested by the platform’s recommendation system, because of which the company saved about $1 billion.

Similarly, Amazon, Flipkart, YouTube and Instagram also employ Machine Learning techniques and tools to cater the needs of customers in a better manner.

Carat

Carat app provides personalized battery life-saving recommendations through the implementation of Machine Learning techniques. The app takes real-time data from your device, combine and compare with others anonymized data and send effective tips to save your phone’s battery life – besides turning the brightness down. Apart from this, the app learns and tells the users when any of the mobile apps is broken and needs to be re-downloaded, or when the phone has to be restarted.

SnapChat

SnapChat is using Machine Learning and Augmented Reality to let us revamp our pictures with different enticing filters. The app camera detects our face, localizes the facial features and adds filters accordingly.

How to integrate Machine Learning into your mobile app?

Different ways industries are using Machine Learning

Machine Learning is an integrative field, with a wide range of possibilities in every business vertical. To make it easier for you to analyze how it will benefit your business, let’s look at the benefits/applications of ML technology according to different industries:

Machine Learning in the e-commerce sector

Machine Learning is opening new doors of customer service and revenue generation for e-commerce brands through services like product search, image recognition, shipping cost optimization, fraud prevention, wallet management, supply and demand prediction, etc. The best examples are Amazon and eBay.

The ML tools gather and analyze the customer behavioral data during the searching and purchase process, their search history, business target and semantic outcomes to differentiate between various good and queries, and serve them with better recommendations. Machine Learning also allows the brands to amalgamate the trends and sales information from different sources like social media and blogs to predict the trend in real-time.

Besides this, ML technology also plays a crucial role in monitoring of online activities and detecting the frauds.

Machine Learning in the Fitness industry

Machine Learning is also making a significant presence in the fitness industry. By retrieving the personal details of the users, Machine Learning is offering them services as per their personal needs and physical conditions. Apart from this, it is empowering the coaches to create a workout routine for their clients as per their goal and body capability, saving enough time to improve their form and technique. For example, Optimize Fitness is a fitness mobile application that practices advanced Machine Learning concept to customize app user’s fitness activities into a personalized experience by serving them with comprehensive instructions and videos of warm-up, exercises, etc.

How to integrate Machine Learning into your mobile app?

Machine Learning in the Healthcare domain

Machine Learning helps in accelerating the process of manufacturing/discovery of a new drug which is quite time-taking and expensive when done manually. It is also beneficial in determining the different kinds of genetic markers and genes so as to provide personalized medication/treatment to the patients. Besides these, ML also offers you a myriad of facilities like disease diagnosis, robotic hair transplantation, epidemic outbreak prognosis, and smart health electronic record.

In fact, there are various healthcare apps based on ML that study the health history as well as diagnosed data of the patient and help the medical specialists to offer real-time advice to the patients.

Machine Learning in the Entertainment industry

As in the case of Netflix, Machine Learning recommends personalized content to the users based on their viewing history and behavior. It is empowering the entertainment providers to understand what sort of creative content users would genuinely wish to see and how to present the home page to the users so as to bring higher profit to the business.

Besides this, Machine Learning is also making a great impact on the advertising and trailer creation process. For example, the trailer of the movie ‘Morgan’ was created using Machine Learning techniques and APIs through IBM’s Watson platform.

Machine Learning in the Education Field

In the education sector, Machine Learning is helping teachers and students in understanding the concepts easily through real-time translation and bot personal tutor services. The ML-enabled chatbots answer the students’ queries, check their assignments and provide unbiased scores/grades based on their performance. Based on the data collected, they suggest better learning techniques and material to the students. They also helps students in finding the right career for them. Apart from these, the ML tools are also equipped to analyze why a student is finding difficulty in writing and help them with the same.

How to integrate Machine Learning into your mobile app?

Machine Learning in the Finance sphere

Presently, more than 90% of the top-reputed financial organizations are relying on Machine Learning and advanced analytics for its working. Banking and finance companies are using Machine Learning algorithms to scrutinize customers’ previous transactions, social media activities or borrowing history to determine the credit rating. Besides this, they are employing ML concept to predict the future trends and detect frauds, and offer personalized customer services at lower cost, higher revenue scope and better compliances.

Machine Learning in Enterprises

Enterprises have to deal with multiple records (records often have information of the same user in different formats and versions) to offer customized services to the users. This process of discovering and integrating the data into a single entity is quite tough with traditional, manually-driven procedures. For this, the enterprises rely on the Machine Learning technology.

ML handles highly repetitive data preparation process with unprecedented speed and precision and helps in sending the right offer to the right customer at the right time. Besides this, the Machine Learning lets them automate management process, improve sales performance, and make better marketing strategies.

As per a survey conducted by Accenture Institute for High Performance, Machine Learning is redefining the enterprise with 40% of the surveyed companies use Machine Learning for improving sales and marketing performance and 76% say they are overachieving their sales targets due to ML. In fact, various European banks are enjoying 10% hike in new product sales with a 20% lower churn rate, all because of Machine Learning.

Process of building a Machine Learning based mobile application

Due to the unfolded features and benefits of Machine Learning, there is an urge in the app developers to learn and integrate Machine Learning in their mobile applications. However, it is quite tricky for them to get the inside out of the technology and start from the scratch due to various challenges, including in-depth mathematical concepts, fragmentation of frameworks, lack of debugging tools, multiple approaches to every single problem, etc.

To help out, tech giants like Google, IBM, Microsoft, and AWS are actively investing in Machine Learning and other fields of Artificial Intelligence. They are offering open-sourced AI/ML libraries and tools which app developers can inherit in their mobile app development processes and build innovative applications easily.

Some of the popular platform services offered are:

TensorFlow

TensorFlow is an open-source software library created by Google for performing numerical computation using data flow graphs. With its flexible architecture, it empowers the app developers to execute computation to numerous CPUs/GPUs in any device with a single API.

CoreML

CoreML is a Machine Learning framework that enables the iOS app developers to integrate wide-range of Machine Learning models into their applications. Besides this, it also supports conventional models and offers a higher performance and efficiency.

Microsoft Cognitive Services

This Microsoft Cognitive Services toolkit empowers the developers to build smart apps that can see, listen, understand, interpret, and speak using natural communication means. It includes:

  • Face API: This API detects human faces and compares them with similar apps to find visually similar pictures, examine previously tagged people as well as check the emotions in images.
  • Emotion API: The Emotion API takes facial expressions as an input to understand the emotions of the customers while using your app and helps in making a better user engagement strategy.
  • How to integrate Machine Learning into your mobile app?
  • Content Moderator: This API monitors the content generated by a user (text, images, videos, etc) at any social media platform, gaming environment or chat and messaging platform to filter out abusive and unbidden content.
  • Computer Vision API: It lets the app developers understand the content of an image to create tags and write meaningful information describing it.
  • LUIS: Language Understanding Intelligent Service (LUIS) is a Machine Learning-based service designed to help app developers instil Natural Language Processing (NLP) features in their mobile apps, chatbots and IoT devices. By integrating the Azure Bot Service, it helps in building high-level bots.

Amazon Machine Learning services

Amazon ML services allow the mobile app developers to create ML models using visualization tools and wizards. Amazon Machine Learning services also offer various APIs to app developers for obtaining predictions for mobile application without administering any infrastructure or executing custom prediction generation code. For example, Amazon ML REST API is used for finding patterns in data and is used for forecasting demand, click prediction and fraud detection.

TCS Ignio

Ignio is an ML-based self-learning platform designed to automate and optimize IT operations. When integrated into your mobile application, it will not just grasp the environmental information for lowering down the chances of knowledge gaps across operation teams and technologies, but will also resolve common errors on its own. And in case it is not able to fix the glitch, it will pass the problem to a human for a solution while training itself to learn how to solve the issue in future.

In a nutshell, Machine Learning is a vast and feature-rich concept, and undoubtedly the most profitable field to invest for a better future. If you are also planning to build a Machine Learning-based app or integrate ML elements in your existing application, you can reach AppInventiv.

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About the author
Sudeep SrivastavaCo-Founder & CEO at Appinventiv - A Google Developer Agency

Sudeep Srivastav, the CEO of Appinventiv, is someone who has established himself as the perfect blend of optimism and calculated risks, a trait that has embossed itself in every work process of Appinventiv. Having built a brand that is known to tap t...

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