NEW YORK, NY / ACCESSWIRE / November 3, 2020 / The impact of AI on COVID-19 has been widely reported across the globe, yet the impact of COVID-19 on AI has not received much attention. As a direct result of Covid-19, AI enterprises are enhancing their strategies for digital transformation and business automation.

Data is the core of any AI/ML development. The quality and depth of data determines the level of AI applications. Considering that the better the data that goes into building the ML training model, the better the output. ML teams need to go through proper data preparation such as data collection, cleansing and labeling.

Data labeling is a simple but difficult task

When it comes to data labeling, the essential step to process raw data (images, text files, videos, etc.) for computer vision so that machine learning models can learn from the labeled dataset, some data labeling companies were forced to move to a work-from-home model due to the pandemic, which has posed challenges in terms of communication, data quality and inspection. For example, Google Cloud has officially announced that its data labeling services are limited or unavailable until further notice. Users can only request data labeling tasks through email but cannot start new data labeling tasks through the Cloud Console, Google Cloud SDK, or the API.

Insiders say that data labeling is a simple but difficult task. On one side, as soon as the labeling standard is set, data labelers just need to follow the rules directly with patience and profession. On the other side, however, data labeling is meant to pursue high quality for ML which demands accuracy, efficiency and high cost of labor and time regarding the massive amount of data to be labeled.

A majority of AI organizations said the process of training AI with data has been more difficult than expected, according to a report released from Alegion. Lack of data and data quality issues become their main obstacles to AI application.

An automated data labeling platform aims to transform the industry

To deal with such issues, Bytebridge.io has launched its automated data labeling platform this year. It aims to provide high quality data with efficiency through a real-time workflow management for AI developers so as to free them from the pressure of data preparation.

An autonomous driving company in Korea needs to label roadblocks and 2D bounding boxes for cars. Considering data security, they have built in-house labeling team. However, they ran into a couple of unexpected problems due to improper labeling tools and low efficiency. Upon trying Bytebridge, their project managers are able to improve working efficiency through Bytebridge's online real-time monitoring function. The number of monthly labeled images has increased from 600k to 750k and they are able to save 60% of budget.

On Bytebridge's dashboard, developers can upload raw data and create the labeling projects by themselves. They can check the labeling status and quality anytime, even the estimated price and time required. Such an automated and online platform greatly ensures labeling efficiency and quality. Bytebridge's easy-to-integrate API enables continuous feeding of high-quality data into machine learning systems. Data can be processed 24/7 by the global contractors, in-house experts and the AI technology.

"We want to create an automated data labeling platform that helps AI/ML companies to accelerate their data project and generate high-quality work," said Brian Cheong, CEO and founder of Bytebridge.io.

CONTACT:

contact: support@bytebridge.io
website: bytebridge.io
company: bytebridge
phone: 010 - 53673971

SOURCE: TTC Foundation