Labeled data is a kind of annotated data used in machine learning, and it indicates the desired outcome, or goal, for the model’s predictions. Data labeling generally refers to activities such as data tagging, annotation, categorization, moderation, transcription, or processing.
To clean, arrange, or classify data, organizations use software, procedures, and people. You have four broad possibilities for your data tagging workforce:
- Employees – They are either full-time or part-time members of your payroll. The tagging of data may not be included in their job description.
- Monitored teams – Data labelers are verified, educated, and actively managed (e.g., CloudFactory).
- Independent contractors are temporary or freelance laborers.
- Using a third-party platform, you access a huge number of employees simultaneously.
Data labeling encompasses a variety of tasks:
- Employing technology to enhance data
- Assurance of data labeling’s quality
- Iterations of a process, such as changes to data feature selection, job advancement, or quality assurance.
- Management of data labelers
- Instruction for new team members
- Success assessment, process operationalization, and project planning
Although the phrases are often used interchangeably, we’ve discovered that accuracy and quality are distinct.
Accuracy in data labeling quantifies how near the labeling is to the ground truth, or how closely the labeled characteristics in the data correspond to actual situations. This is true whether you’re creating models for computer vision (such as recognizing objects in photos) or NLP (natural language processing).
Accuracy is essential to the quality of data labeling throughout the whole dataset. Does the work of each of your labelers seem identical? Are labels correct across all of your datasets? This is applicable regardless of whether how many data labelers functioning concurrently.
Low-quality data might backfire twice: first during model training, and again when your model uses the labeled data to drive future judgments. To construct, verify, and sustain the production of high-performing ML models, it is necessary to train and validate them using trustworthy, dependable data.
Our Github
