Over the past few years, numerous industries, including healthcare, finance, and entertainment, have undergone a transformative shift thanks to artificial intelligence and machine learning. The growing need for intelligent applications and the widespread use of mobile devices mean mobile devices need machine learning capabilities. This blog will cover Core ML, Apple’s framework for incorporating machine learning into mobile devices. We’ll go into the details of what it is, its framework, and pros and cons of using the framework.
Introduced by Apple in 2017 as part of their iOS 11 release, Core ML is a machine learning framework used across Apple products (iOS, macOS, watchOS, and tvOS) for performing fast prediction or inference with easy integration of pre-trained machine learning models on the edge. This framework allows for real-time predictions of live images, video on the device, or the creation of intelligent applications that can perform complex tasks such as image recognition and natural language processing.
A Core ML model is a result of applying a machine learning algorithm to training data. Models can be built using the Create ML app bundled with Xcode. In this case, no conversion is required as models trained with Create ML are already in a format suitable for use in an app. Additionally, there are tools and third-party machine learning libraries available for converting models into Core ML’s format if built with other libraries like TensorFlow, Caffe, ONNX, or PyTorch. Now that we’ve given the details on core ML, let’s dive into the framework.
The Core ML framework is built on three main technologies:
1. The CPU (Central Processing Unit): This works with memory-intensive procedures such as natural language processing (NLP).
2. The GPU (Graphics Processing Unit): It is well-suited for computation-intensive processes, such as working with images and object detection.
3. The ANE (Apple Neural Engine): This framework was designed to accelerate neural networks.
One of the notable features of Core ML is its ability to allow switching between the CPU, GPU, and ANE on the fly. This optimization enhances on-device performance, which eliminates the need to manually select the processing unit for each model.
Core ML supports a wide range of machine learning tasks, including image and video analysis, natural language processing, speech recognition, and even augmented reality. By leveraging Core ML, developers can enhance user experiences, improve app performance, and enable intelligent features that were previously only feasible on cloud-based systems. Using Core ML has pros and cons, which we’ll highlight below.
There are both advantages and drawbacks of integrating Core ML into mobile applications. Below we have a comprehensive analysis:
1. Easy to use: Core ML simplifies model integration into applications, which usually requires significant effort when preparing data and training models. for
2. Low latency and near real-time results: Eliminates the need for network API calls. This is critical for applications like video processing from the device's camera.
3. Offline availability, privacy, and cost-effectiveness: Applications run without network connection. There are no backend servers, and data never leaves the device.
4. Support: Supports a wide range of machine learning tasks, enhancing app capabilities across various domains.
1. Increased application size: Adding models to the device increases app size, especially with larger and more accurate models.
2. Computation-intensive tasks drain battery: Prediction and inference on the device require significant computation, potentially leading to increased battery drain, particularly on older devices.
3. Model training and updates: Models often need continual training outside the device with new user data. Updating the app with new models may strain network transfer for users.
Core ML stands as a pivotal technology bridging machine learning and mobile applications. By leveraging Core ML developers can unlock a multitude of possibilities from real-time image recognition to natural language processing, all while ensuring offline availability and privacy.
It should be taken into consideration that Core ML comes with its own set of challenges - increased app size and battery drain which make careful optimization necessary. Despite these challenges, Core ML remains a foundation for advancing mobile app capabilities and enabling developers to create immersive and intelligent experiences . As the demand for intelligent applications continues to grow, Core ML is primed to play an increasingly vital role in shaping the future of mobile technology.
