With the rise of advanced technologies such as machine learning (ML) and artificial intelligence (AI), the demand for accurate data labeling has also increased dramatically. Labeling data is a significant data tagging process that adds context to data before using it in model training.
Exploring in more depth, labeling data is the process of identifying raw data, which can come from images, text files or videos, and adding informative labels to give context so that an AI or ML model or different algorithms can learn from it.
Data labeling usually starts by asking humans to make observations about given pieces of unlabeled data. For example, labeling specialists may be tasked with tagging all the images in a dataset that features a motor vehicle. The data tagging process can be as simple as yes or no or as detailed as identifying the pixels in the image associated with the vehicle. The ML model uses human-provided labels to essentially learn the underlying patterns through the process of model training. As a result, the trained model can be used to make predictions on new data.
For businesses to effectively label data, they need to hire an experienced specialist in labeling data who can lead the process.
Skills and Responsibilities of a Data Labeling Specialist
The data labeling specialist role is an expert position in the machine learning and artificial intelligence field. The role mostly involves labeling training data to feed AI and ML data labeling algorithms.
Specialists offering data labeling services require the ability to focus for long periods, consistently pay attention to small details, and spend the workday using a computer platform. When recruiting a data labeler, the following main responsibilities of a data labeling specialist should be kept in mind:
- Manually assign meaningful labels to separate data pieces
- Assist in keeping a business informed of regulatory labeling requirements
- Interact with computer vision, AI or ML engineers to feed information about data into computer systems
- Feed algorithms with information about data pieces
- Reviewing all labels for accuracy and adherence to regulations
- Provide insights gained from the labeling process
In order to complete the above tasks, the skills of an expert data annotator specialist should ideally include the following:
- Ability to effectively label machine data
- Knowledge of post-process audits and writing error reports on data
- Ability to identify and successfully resolve the cause of low quality and give feedback to teams on product improvements
- Understand the nature of data protection and how to deal with breaches of data
- Detail-oriented with the ability to follow complex instructions
- Capacity to focus for long periods
- Ability to work as part of a team and to oversee data labeling tasks
- Excellent oral and written skills
Some companies that need more complex and advanced data annotation services may need a specialist with even more skills and experience. Sought after data tagging skills may include experience with labeling large datasets, knowledge of deep learning algorithms, familiarity with SQL and ability to work with cloud applications.
The Role Data Labeling Plays in a Company
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The labeling of data is essential for any modern business that works with technology because computers are bound by multiple limitations. But more importantly, not all of them can work without human intervention.
A computer system can initially be programmed to perform activities that do not require human hands. However, that same program will not have the capacity to distinguish between a cat and a dog without putting effort into training the computer. Hence, for algorithms to learn based on the dataset presented, supervision is required.
The Main Benefits Data Labeling Can Bring to a Business
Companies hire specialists that provide labeling services not only to assist with data collection and labeling activities but also to get plenty of other benefits. A specialist in labeling data can help a business meet its data tagging needs in the following ways:
- Assists to improve data accuracy
Labeling data can dramatically increase the accuracy of data used to train machines and to run algorithms. Different data sets used to train AI and ML algorithms can learn different types of factors that assist the model to utilize its database to present the most fitting results in different important scenarios.
- Helps to enhance the quality of training data
Modern techniques involving labeling data can assist businesses to improve the quality of training data interactively after human correction. Through accurate data labeling, AI and ML systems take less time and offer greater output.
Effective and accurate liberalization and data annotation and labeling ensure better quality assurance within AI and ML algorithms, allowing the model to train and yield excellent output.
- Assists in uncovering new business opportunities
Labeling data allows AI and machine learning algorithms to build an accurate understanding of real-world conditions. As a result, labeling data can help businesses to automate their decision-making and uncover new business opportunities.
The Bottom Line
Data is often talked about as the future of the business world. With advanced technology such as AI and machine learning slowly seeping into our everyday lives, data and its proper use can make a significant impact on the success of businesses. Data annotator and labeling teams that use data tagging provide businesses with greater context, better quality and excellent usability.
