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Data Labeling refers to the tags or labels allotted to differentiate and add significance to the content to develop the AI models. The data labels indicate whether the content is a text, image, video, audio, etc. The futuristic technology; Artificial Intelligence (AI) and Machine Learning (ML), depends on the data scientists and engineers who spend immense time on data preparation rather than the actual model itself. Various approaches and best practices must be considered for an efficient data labeling process.
CHALLENGES FACED DURING DATA LABELING
1. Team Management
- training the new members for various tasks
- difficulty to understand the domain and the specific task or subject
- assigning the tasks appropriately to carry out the work flawlessly
- technical issues
- ensuring easy communication and collaboration with data and team members
- quality control and data validation
- cultural, geographic, and language barriers between the team can hinder the work process.
2. Data Management
Subjective data issues: personal biases and socio-cultural differences may also lead to communication problems within the team. The change of team members may also create issues for the newcomers concerning data.
Objective data issues: the inaccuracies due to data illiteracy may lead to the generation of faulty data tags or labels in machine learning that will be misinterpreted by the AI.
3. Right Tools and Strategies
4. Ensuring a Cost-effective Process
5. Conceding with Data Security Standards
Working with AI and Machine Learning requires meeting certain data security standards as stated by General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Data Processing Agreement (DPA). Data confidentiality compliance and security regulations are increasing globally. Data labeling companies are bound to comply with internal data security and privacy standards. The failure to meet the data security regulations may hinder your company’s work and data Labeling process.
BENEFITS OF DATA LABELING
- Data Consistency
In-house tools can produce consistent data results and long-term reliability.
- Feedback system
An annotation feedback loop ensures constant performance monitoring and improvement.
- Auto Data Labeling
Automatic data labeling is done using various programmatic algorithms that are task-specific or object-specific in ML algorithms. The process reduces the cost and time and uses software to accomplish the task.
- Deep Learning
Deep learning, along with ML algorithms can analyze and label unstructured data with ease. Deep learning can excel in data labeling over a relatively short period of time and prevents unnecessary costs.