One of the very recent announcements from Amazon was the release of their new tool they called Amazon Rekognition Custom Labels. This advance tool has the ability to improve machine learning on a whole new scale, allowing for better data analysis and object recognition.
This tool will help users to train their machine learning models more easily and allow them to understand a set of objects out of limited data. Simply put, this capability will make machines more intelligent and capable of recognizing items with fewer data set than ever before.
The Perks of Machine Learning with Amazon’s Rekognition
Machine learning holds a scientific study and adaption of algorithms that allow computers to discover new information and functionalities without needing direct guidance. Otherwise speaking, machine learning has the ability to understand on its own.
So far, the machine learning model demands large data sets to learn something new. Such as, if you want a device to recognize a table as a table, you would have to provide hundreds of tables, maybe even thousands of pieces of visual evidence of what a table looks like.
Still, with this unique tool, machine learning systems will be able to work with very inadequate data sets and still completely learn the distinction between new items and objects.
Machines will now be able to recognize a group of objects based on as little as ten to fifteen images, which a notable improvement as compared to previous requirements. Amazon is steadily stepping on a fresh and untracked path of machine development.
Why Amazon Rekognition is Important
Having insufficient data to work with used to be a hurdle in machine learning. Today, a new model will be able to learn efficiently without large sets of data all gratefulness to Amazon’s recently announced tools.
Amazon’s blog post stated, “Instead of having to train a model from the mark, which lacks specific machine learning expertise and millions of high-quality marked images, customers can now use Amazon Rekognition Custom Labels to deliver state-of-the-art production for their individual image review needs.”
The new Amazon Rekognition featured on the 3rd of December and it is expected to bring notable changes to machine learning all throughout 2020. The release of the new tools also took place in the AWS reinvent conference that was held in Las Vegas
Following are the use-cases of AWS Rekognition
- Library of searchable images: Amazon Rekognition makes images searchable, which allows developers to discover objects and scenes that appear in them.
- Facial Verification of users: With Rekognition you will be able to confirm the identities of users comparing their images in real-time with their stored reference images.
- Opinion Analysis: This analysis will be based on emotions such as happiness, sadness, and surprise with the help of facial images. A developer can interpret images in real-time and send emotional traits to Redshift in order to generate reports systematically on inclinations in each online store.
- Face Recognition: With the help of stored IndexFaces function and facial metadata, Rekognition will facilitate the search of your facial image within the API.