Earlier, Artificial Intelligence then IoT and now Machine learning is another technology that is holding the potential to change the daily lives in a new way. The techno-experts claim that if machine learning would be blended with the existing technologies like AI and IoT then it can terrifically give a 360-degree turn to the people’s lives.
But another facet of machine learning is it is not yet widely disclosed worldwide and therefore the technology developers are diving into machine learning to find the new hacks of ML. An aid to this, mobile app developers are attempting to integrate ML with mobile applications.
Here we have tried to aid comfort to the developers who are in the way of mobile app development with machine learning. We have come up with the information of various libraries available in Machine learning market that can be utilized in mobile app development. These libraries are open-sourced libraries that anyone can access them to frame the mobile app development with machine learning.
Let’s know more about ML platforms and libraries:
TensorFlow
For adding the computational and numeric based performance to the mobile apps, TensorFlow libraries are used. It is open source library with extensile platforms and architecture for computational tasks. TensorFlow allows neural network research with the easy codes and it was developed by technocrats of Google Brain team that allows an ease to use TensorFlow in various domains. With TensorFlow the developers can perform deep neural networking with an ease.
Core ML
Core ML is the feature that strongly supports integration mobile apps with trained models. Core ML performs monotonously on the device with apps and consumes less power and memory of the system.
It incorporates the frameworks specific domains as:
Allows decision trees evaluation with GameplayKit library
Serves as the base for Natural Language Processing(NLP)
Image analysis
Cognitive Services of Microsoft
Microsoft offers various cognitive services for the app development technology. Here is the list containing few cognitive services and APIs.
Content Moderator: Content Moderator API efficiently processes the user’s content. It is an API that can be used for rolling out the irrelevant content of that user may feed on gaming, chatting or social media platform.
Face API: A Face API is used for face detection. It detects and compares the faces that are similar. The Face API can be understood by taking Facebook as an example. While tagging any images, Facebook suggests the user about the similar faces the user can tag. This suggestion is based on the Face API performance.
Computer Vision API: Computer Vision API observes the images and its content and then develops essential info and generate tags.
Emotion API: As the name suggests, this API accumulates the user’s facial expressions and acquires the user’s expressions to boost up the user’s attention and contributes to more visitor’s engagement.
LUIS: LUIS allows installation of Natural Language Processing (NLP) features. This ML service supports all mobile devices, chat-bots and IoT devices.
TCS Ignio
In the series of Machine learning, TCS has its own platform named as Ignio. The Ignio of TCS efficiently optimizes IT operations of the company. Once collaborated with the mobile apps, this machine learning feature can easily lower the risk of information gaps. TCS Ignio is self-sufficient and flexible to learn to resolve various complex problems handling lots of data.
ML services of Amazon
Machine Learning has been widely utilized by Amazon. ML supports various visualization and wizards tools that Amazon uses for improvising its services for the users. Machine learning services of Amazon have hovered various APIs for data predictions based on the data analysis of the user’s choices and approach. This allows the app developers to predict and feed their apps with the data their users want. For example, Amazon’s NetFlix displays ads on the basis of fed data and the user’s watching history as “You may like” for the users watching videos and movies on it. This is all done with training the machine with the data analysis process and making decisions based on the available data.
Conclusion
To epitomize, we will witness that Machine learning will occupy the large area of IT sector and mobile app development industry. The mobile app developers are moving ahead to this new technology of machine learning. They are now grasping the potential and calibre that the machine learning holds to utilize the accumulated data to resolve the prediction problems.The mobile apps with machine learning would be capable of performing the tasks and computational operations based on the data gathered in the devices and with artificial intelligence and deep neural networking the apps will be able to make the next possible decisions for the users.
For any queries and idea, we at Hvanage Technologies with our technical team is always present to serve you with the best and latest technologies.
