Machine learning has displayed its artistic potential by creating several art pieces and may soon lead to an artificial intelligence-powered art movement. Similarly, the potential of machine learning can be witnessed across several industry sectors such as retail, healthcare, finance, manufacturing, and tech. Following the trend, mobile operating systems such as iOS and Android use their own machine learning framework called CoreML and TensorFlow. With machine learning, tech giants like Apple aim to improve security, user engagement, and user experience. Also, CoreML can help improve features like Siri and deliver personalized in-app ads. Independent developers can also use CoreML framework in their own apps to leverage machine learning.
CoreML framework makes it easier for developers to integrate machine learning capabilities in their iOS apps. This framework supports features such as Siri, QuickType, natural language processing, camera, and image analysis in mobile operating systems like iOS. Prior to CoreML, developers had incorporate pre-trained machine learning models and port them with the help of frameworks like Accelerate or metal performance shaders. CoreML still uses Accelerate and metal performance shaders under the hood. However, CoreML accurately decides between the two frameworks based on its applications.
CoreML essentially performs two major tasks i.e. training and inference. Training process includes gathering and optimizing relevant data, defining data models, and training the system. After training, machine learning models can deliver accurate predictions based on the data provided in the training set. After gaining satisfactory results, machine learning models can be used for inference. In this process, machine learning models can analyze data apart from the training set and generate precise results. With this approach, CoreML can be used for the following features:
CoreML can be used to integrate various functionalities such as facial recognition, object detection, image alignment, barcode detection, and object tracking on an iOS app. These functionalities can be used to identify users, barcodes, and objects.
Natural language processing uses machine learning for language identification, named entity recognition, and lemmatization. With CoreML, mobile operating systems such as iOS can implement natural language processing to understand text responses in digital assistants.
The following types of businesses can utilize CoreML in mobile operating systems like iOS to implement machine learning:
Machine learning can be a significant element of an e-commerce mobile app. Machine learning models can be trained using a customer’s purchase history and personal information. With the help of customer data, machine learning models can help suggest relevant products to customers. Machine learning can also be used for suggesting products related to their purchase during checkout. For example, if a customer is buying a laptop, then the e-commerce app can suggest a hard disk along with it. Additionally, e-commerce apps can use machine learning for natural language processing in chatbots. With the help of natural language processing, chatbots can precisely understand the context of a customer’s query and answer questions accurately.
Healthcare organizations across the world are preparing for widespread machine learning adoption. Healthcare institutions can develop iOS apps that can analyze a patient’s symptoms and suggest treatments. For this purpose, CoreML can analyze symptoms using patient data and available data about various disorders to predict disorders. Also, healthcare apps can track fitness regimes, diet, weight, and height of users to suggest suitable workout plans. With this approach, CoreML can help deliver personalized healthcare for iOS devices.
According to McKinsey, almost 82% of Fortune 500 executive don’t believe that their organizations hire talented people. In this scenario, machine learning can be a useful alternative to traditional recruitment methods. The deployment of machine learning can help recruitment agencies find right individuals for different jobs. CoreML can be used in online recruitment apps to suggest relevant jobs to candidates based on their location, qualification, experience, and CTC. Also, machine learning can help in screening candidate resumes by automatically filtering candidate experience, skills, and employer information. Hence, CoreML can simplify the beginning stages of recruitment process for employers as well as candidates.
Several travellers use mobile apps to plan their trips and book hotels and flight tickets. However, manually going through multiple hotel listings and flight schedules can be a tedious task. Travel apps with CoreML can automate the entire planning process and suggest the least expensive hotels and flights. Also, machine learning models can learn with the help of user data to suggest a personalized travelling experience. Such personalized travel plans will align with a user’s preferences and schedule. For instance, if a traveller prefers eating vegan food, then a machine learning-enabled mobile app can suggest hotels and restaurants with vegan food options.
Food delivery services and restaurants can integrate CoreML in their apps to analyze eating habits of their customers and suggest dishes and cuisines accordingly. Machine learning can also be used by chatbots to interact with customers and to predict ETA for food orders by analyzing traffic conditions. For example, if a customer’s delivery will be late due to traffic, machine learning algorithms can help notify that customer about the delay.
Financial services can release finance tracking apps that can help users save money. These apps can track a user’s spending habits and transaction history over a specific period of time to generate a money-saving strategy. Apps with CoreML can also offer appropriate deals and rewards programs to help users save more money.
These businesses can use iOS apps with CoreML to enhance user experience and boost revenue. However, integrating CoreML into an iOS app requires large volumes of data and expertise in coding and specific programming languages for mobile operating systems. Hence, business leaders need to work with skilled developers. Businesses can reach out to Digital Fractal to develop machine learning-enabled mobile apps.