ByteBridge

By building an automated data processing platform, Bytebridge.io provides revolutionary data solutions for machine learning industries.

If data annotation service is meant to accelerate development of machine learning industry, why not make it in an automated, transparent platform where developers can manage and control the project in a dynamic way? ByteBridge.io makes it possible through blockchain technology and automated dashboard that eliminates the unnecessary costs and process.

Data annotation technique is used to make the objects recognizable and understandable for machine learning models. It is critical for the development of machine learning (ML) industries such as face recognition, autonomous driving, aerial drones, and many other AI and robotics applications.

This is a data labeling platform with robust tools for real-time workflow management, providing high quality data with efficiency. Core strengths include: 1. Seamlessly manage all projects with powerful tools in real-time 2. Significantly lower project costs with transparent standardized pricing 3. Individually decide when to start your projects and get your results back instantly

Data labeling applies multiple labeling tools to process data, the basic element of AI, so as to make data understandable for computer version and "teach" AI to identify, judge and act like human beings. If data serves like oil for AI, data labeling is to refine crude oil into gasoline. However, compared to the fancy high-tech AI, data labeling is labor-intensive in essence. Considering their great contributions to fueling AI industry, data labelers deserve more attention to improve their treatment and social status. Bytebridge.io, a blockchain-driven data company, has also realized such urgent problems in the data labeling industry and committed itself to powering AI development through its automated data labeling platform. @habib_ullah1

Used Bytebridge for a research project in NLU recently. Since I was focusing on intent classification and slot tagging, I needed some annotated training data to train the model. I contacted the customer service team at Bytebridge and they handled the task really well. Their team include some experts in the field (which is really special for a data labeling company) and even gave me some practical advice for my research. Here's one of the tasks they did for me as an example: To build the NLU component we need real user data and design tasks where the user’s goal was to answer questions about how people would react in some scenarios designed in advance, for example: calendar, email, news, and weather. In any given scenario, the questions put to workers are structured to capture the requests and the Bytebridge team even provided some example answers for easier understanding. The online staff will then enter their answers to these questions and pick possible entities for each of their answers from the pre-designed proposed entities list. The data is then organized in CSV format which includes information like scenarios, intents, user answers, annotated user answers etc. The final training data is accurate and I got it in a really short time. Plus the $50 credits is great especially for phd students. Awesome platform! Thanks again for the great service.

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.