Create ML and Core ML - Apple’s Strategy to Remain a Leading Tech Brand

‘Apple’ is considered one of the leading tech brands in the world. It has accomplished millions of customers through its unique strategies. Apple products are defining a sophisticated tech experience for their user. The way Apple has left the impression of standard and attractive product services makes us wonder about its fascinating technology and marketing secrets. Apple utilizes machine-learning models which entail createML and CoreML to enable intelligent features and achieve a better user experience.

Create ML and Core ML are supervised machine-learning tools used to train, deploy, and target-trained models in real-time. This enables to build, training, and deploy reliable machine-learning models on Apple devices such as iPhone, iPad, Apple Watch, and Mac apps.

Both Create ML and Core ML are quite similar and work on labelled datasets; they utilize supervised machine learning algorithms that adapt best to the Apple user interface.

Create ML

Create ML was a framework introduced by Apple in 2018. It eases the complex machine learning training to build Core ML models that are easily accessible to everyone. We can train models performing tasks such as image recognition, text extraction, and identifying relations between numerical values.

Intelligent Document Solutions provides assistance in keeping the documents updated and organized. Apple develops ML models mainly to create content and improvise business suggestions.

We can train multiple models using different datasets within a single project. The Create ML offers to pause, resume and extend the training process at the user's convenience.

Core ML

Core ML has gained a lot of attention in recent years. It utilizes machine learning algorithms to a set of training data to build a model. The Core ML models make predictions based on the user input. This helps developers focus on testing operations as it reduces the time to design and train models.

The Core ML models perform complex tasks, for instance categorizing photos and identifying specific objects in images. Core ML models run on the user’s device, which does not require any internet connection, enabling the user data's privacy.

5 Machine Learning Applications in Apple Devices

Face Id Authentication in Apple

Face Id technology uses facial recognition patterns to develop a specific face model. This way, an iPhone can quickly identify your face to unlock your device. Apple utilizes vision technology - a deep learning framework to detect features of users’ faces based on previously recorded data.

ML in Apple Pencil

Handwriting recognition is an advanced machine learning technology used in iPad. If you have an iPad, the device records your writing style and speed through an Apple pencil. It utilizes pressure-sensitive touchscreen automation to detect the palm movements of your hands.

ML in Voice Assistance

The first virtual voice assistant ‘Siri’ was developed by SRI International and the Swiss Federal Institute of Technology in collaboration. Siri was a milestone in artificial intelligence history. This is a segment of the Cognitive Assitant that Learns and Organizes (CALO) program’s DARPA Personalized Assistance that Learns (PAL) project run by SRI. Siri has become part of many people’s daily life that quickly performs smart operations.

Image Sorting and Emojis

Image sorting is one of the best advancements in machine learning that Apple has offered to its users. Sorting out images based on people, time, location, and various travel categories has enabled easier access to images on Apple devices.

Emojis have secured a vital role in social media life. Apple has moved a step ahead by adding animated emoticons to iPhones and emoji editing features. Now we have more to express in Emoticons while texting.

ML in Apple Watches

Apple Watch has made it easier to track your sleep schedule and patterns. The watch even records your daily activities and calorie expenditure during workouts. It keeps track of your running routine with footsteps, time spent, body velocity, and distance covered.

In addition to these features is handwashing recognition with a 20-second timer where the sensors record handwashing movements and sounds of water flow. You can easily set up hand washing recognition in just a few taps.

Hence, these were the few ML applications in Apple products; the development and upgradation of these ML-related services are still emerging, and we may anticipate a variety of future ramifications.

Final Thoughts

Apple is unstoppable in innovating better technologies, keeping users curious about its new intelligent features. No doubt that Apple has strived to remain in the top position across the globe in the tech industry. This blog is a glimpse of machine Create AL and Core ML. Machine learning is a vast and diverse technology in artificial intelligence that has paved way for unimaginable technological experiments.

To learn more about machine learning, you should check out Advanced Artificial Intelligence and Machine Learning program.