Top 5 Features of Core ML to Help Develop Innovative iOS Apps

The tech giant, Apple is integrating artificial intelligence and augmented reality as part of its future offerings. The latest technology added to this is Core ML, an all-new machine learning framework that enables iOS app developers to run machine learning models hassle-free on iPhone, iPad and other Apple devices.

Now, businesses from various industries like healthcare, education, eCommerce and more can leverage this framework to boost the app performance. Further, it will allow them to develop applications with advanced features through just a few lines code.

The most significant benefit of Core ML is it enables AI tasks to execute quickly on iPhone, iPad, and Apple Watch. It will also elevate other features like face recognition, text analysis, rectangle detection, bar code detection, object tracking and more.

Let’s have a look at other ways in which Core ML will drive innovation to your iOS applications:

• Advanced Machine Learning Capabilities: Apple is at the forefront of exploring advanced analytical and AI techniques like Metal Performance Shaders, Neural Network Subroutines and more. Core ML by Apple is also the very first framework that amalgamates state-of-the-art features. They include natural language processing, advanced image processing, and data computation.
• Supports Multi-Layered Model: The latest version of Core ML comes with thirty different layers to support the integration of multiple complex models into your app. These models vary from tree ensembles, vector machines, and other core models including advanced neural
  network variants.
• Enhanced Security and Resource Allocation: The Core ML framework integration brings new-age machine learning models to your iOS app. They not only improve the security of the app but also enable development of far-reaching applications with better resource allocation – RAM and power.
• Offline Support: When there are machine learning models integrated into the iOS app, they do not have internet connection dependency for computing their needs. It enables them to
  do their job in the offline mode as well.
• Seamless Third-party Integration: The codes for Machine learning algorithms have a vast variety of languages. They range from Java, Scala to Python on one side and statistical modeling tools like SAS on the other. All these amalgamate hassle-free can with your iOS mobile app.

Real-world Application 

When we talk about the application of Machine Learning in the real world, there are a plethora of its use cases. Many companies are using this technology for building conversational UXs or virtual assistants, also known as AI chatbots. There are many popular apps with virtual assistants.

With machine learning tools, you can also send smart notifications about your offer or deal of the day. Voice assistants can send voice messages about hot prices and special deals. It helps your business to be more proactive. It also reminds the customers about your brand, and help engage even the churned clients.

Conclusion – The Core ML framework is enabling enterprises to give inception to their creative ideas. It is empowering them to take their application development for iOS to the next level.