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Know How to Implement Machine Learning into Android Apps – Analytics Insight

Integrating Machine Learning and Android apps
Today in the world of mobile application, AI and machine learning are the areas that are growing rapidly. Many tech companies are investing in machine learning tools that can enable developers to integrate machine vision into android applications. According to the estimates of Grand View Research, the machine learning market is expected to grow by $18.24 billion at a CAGR of 7.7% by 2025.
Machine vision inspection lets the devices find, track, classify and identify objects in the form of images. The complex machine vision algorithm can allow you to get data from images and assist you in analyzing it. Supervised machine learning can be used for identifying, recognizing, reconstructing 3D shapes and images, and many more.
Most of the manufacturing industries use machine vision for assessing quality and safety. This is done with the help of machine vision cameras which can recognize things of any defects. The three main industries that use machine vision systems are Retail, Agriculture, and Warehouse and logistics. Let us see how machine vision is benefiting them.
Retail is a large sector that is using machine learning to identify pictures that the customers are willing to buy.  When the customer uploads a picture of any product they want, machine vision identifies and searchers for a similar item to fulfill their need. ASOS is the best machine vision technology that helps mobile apps for this purpose.
The agricultural sector uses machine learning to find and identify the types of plants by just clicking photographs of the plant. LeafSnap is the best mobile app that is used for identifying plants.
Warehouses and logistics use machine learning and vision for scanning barcodes. This can make their work simpler and easier.
Machine Learning Applications for Android
Mobile and android apps developers have an edge from innovations that machine learning offers. This is possible because of the technical abilities of the mobile applications that can make things smoother for user interfaces, experiences and empowering them. Users always want their experience to be personalized in the present day. So it is not just enough to create an application but the best one which can satisfy the user’s needs.
Here are a few tools and libraries you can use to implement machine learning and vision in your Android apps
ML Kit
ML Kit is the new framework for machine learning which is presented by Google at I/O 2018. This is one of the easy-to-use frameworks for machine learning. Its Application Programming Interface permits the developers to use text, face, barcode, landmark, object recognition along with image labeling.
OpenCV is the most famous and commonly used machine learning library by developers. It is an open-source library that can store thousands of algorithms for analyzing images. OpenCV can be used to recognize faces of people, text, etc. OpenCV algorithms can be used for gesture recognition, camera recognition, building 3D models, tracking eye movements and videos, etc.
TensorFlow is also a machine learning framework from Google that can be useful for your project, create, and implement deep learning. TensorFlow works on a lot of platforms and for mobile apps, Google has released a new separate library called TensorFlow Lite.
Steps to implement machine learning
Step 1: Collect the training data
Step 2: Alter the data into required images
Step 3: Create separate folders of images and group them
Step 4: Reskill the model with new images
Step 5: Improve the model for accessible mobile apps
Step 6: Embed. flite file into the app
Step 7: Run the app locally and see if it detects the image
Take away
Machine learning is an application of artificial intelligence, through which we can learn, explore and envisage outcomes automatically. This can benefit most industries in developing their mobile and android apps for better efficiency and for giving the users the best experience.

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