The covid-19 epidemic in 2019 disturbs routine life across the globe. Due to the limitation of physical conditions, traditional enterprises are enhancing their strategies for digital transformation and business automation.
Labeled data is the core of the AI/ML industry. The quality and quantity of data determine the performance of the AI model.
It is showed that an in-house experienced team composed of 10 labelers and 3 QA inspectors is able to complete around 10,000 automatic driving lane image labeling in 8 days.
In fact, training a model needs tens of thousands or even millions of no bias data samples, which takes a lot of time.
During the hard period, some data labeling companies were forced to switch to a work-from-home model, 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 hand, once the labeling standard is set, data labelers just need to follow the principles and rules. On the other hand, if the training data has a bias, the algorithm model cannot be well developed, AI company needs to restart the data labeling process again. Timing is important, once the company is behind the schedule, the product may be overtaken by competitors.
A majority of AI organizations said the process of training AI has been more difficult than expected, according to a report released by Alegion. data at scale and quality issues become their main obstacles in AI system R&D.
To deal with such issues, Bytebridge has launched the automated data labeling platform in 2020.
“We want to create an automated data labeling platform that helps AI/ML companies to accelerate their data project and generate high-quality data,” said Brian Cheong, CEO, and founder of Bytebridge.
Accuracy and Efficiency
- Dealing with complex tasks, the task is automatically transformed into tiny component to make the quality as high as possible/maximize the quality level as well as maintain consistency.
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
- All work results are completely screened and inspected by the machine and human workforce.
1. Top 5 Open-Source Machine Learning Recommender System Projects With Resources
2. Deep Learning in Self-Driving Cars
3. Generalization Technique for ML models
4. Why You Should Ditch Your In-House Training Data Tools (And Avoid Building Your Own)
Individually decide when to start your projects and get your results back instantly
- The client can set labeling rules directly on the dashboard.
- Clients can iterate data features, attributes, and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation
- Progress preview: clients can monitor the labeling progress in real-time on the dashboard
- Result preview: clients can get the results in real-time on the dashboard
- Real-time Outputs: clients can get real-time output results through API. We support JSON, XML, CSV, etc. and we can provide customizable datatype to meet your needs
ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.