An Introduction to Data Labeling in Artificial Intelligence

The world is flooding with data. In 2018 alone, we generated over 30 zettabytes of data.

In any AI project, for AI professionals, data issues are some of the sticking points.

Sometimes, data needed for a project may not exist at all. In others, it may exist, but be out of reach – locked in competitors vaults. There are also situations when relevant data is available and can be dug, but it may not be suitable to be fed into the system. This post will explore the intricacies of the last condition.

What makes data suitable or unsuitable for feeding into the computers? The answer lies in data labeling. 

What Is Data Labeling?

It’s not uncommon to have massive amounts of data today. But, if you wish to use it to train machine learning and deep learning models, you will need to enrich the data so it can be used for deployment, training and tuning the model. Training machine learning and deep learning models require huge amounts of carefully labeled data. Labeling raw data and preparing it for feeding in machine learning models and other AI jobs is known as data labeling or data annotation. According to Cognilytica, an AI analyst firm,

Data Wrangling consumes over 80% of the time in AI projects.  

How Is Data Labeled?

Most data organizations have is not labeled, and labeled data is the foundation of AI jobs and AI projects.

Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing. 

Labeled data highlights certain features and classifies it according to those characteristics – that can be analyzed for patterns by the models to predict new targets. For computer vision in autonomous vehicles, for instance, an AI professional or data labeler can use video labeling tools to indicate the location of the street signs, and placement of pedestrians and other vehicles to train the models.

An array of tasks included in data labeling are: 

• Tools to enrich data
• Quality assurance
• Process iteration
• Managing data labelers
• Training new data labelers
• Project planning
• Success metrics
• Process operationalization

Data Labeling Challenges for AI Professionals

In a typical AI project, professionals can encounter the following challenges when undertaking data labeling:

• Low quality of data labels. There can be numerous reasons for the low quality of data labels. One of the most prominent causes of which are three determinants behind the success of any organization or workflow – people, processes, and technology.
• Inability to scale data labeling operations. Scaling becomes a must when the volume is growing and business or project needs to extend its capacity. Since most organizations label data in-house, they usually face difficulty in scaling their data labeling tasks as well.
• Unbearable costs and non-existent results. Organizations and AI project managers usually hire either highly paid data scientists and AI professionals or a group of amateurs to handle data labeling. However, both can backfire easily. Former because they are highly paid professionals can thus take the costs of labeling sky high. Latter because amateurs’ labelers may not be sufficiently trained for the job. The judicious selection of the right professionals is crucial.
• Ignorance of quality assurance. Putting quality checks can provide significant value to data labeling processes, especially at iterative stages of machine learning model testing and validation.

Who Can Label Data?

Training a single machine learning models to take magnanimous amounts of carefully labeled data. Most importantly, those labels are usually applied by humans. According to a survey,

Firms spent over USD 1.7 billion on data labeling in 2019. This number can reach USD 4.1 billion by 2024. 

Such promising predictions for the industry indicate it to be a golden source of employment.

Cognilytica says that mastery in the given subject is not required to perform data labeling. However, a certain amount of what AI professionals say ‘domain expertise’ is crucial. This means even amateurs with the right training can thrive as data-labelers.

Training a machine learning model requires huge amounts of carefully labeled data, and those are usually applied by humans. 

Present Trends: How Are Companies Labeling Their Data?

Big firms use in-house resources to label data. Those that lack the required resources and competency to wrangle the data outsource their requirements to an outside agency.

MBH, a Chinese firm is involved in data labeling for numerous companies.

Amazon’s subdivision, Mechanical Turk, connects small and mid-sized firms to casual workers paid per piece to perform data labeling.

Companies use a combination of software, people, and processes to clean and structure their data. Overall, they have four options for developing capacity:

• Employees. This includes hiring a full-time or part-time workforce including AI professionals, to be involved in various aspects of AI projects, one of which is data labeling
• Managed teams. They are experienced and trained teams of data labelers
• Contractors. They include freelancers and casual workers
• Crowdsourcing. Finally, companies may use third-party platforms to access the huge workforce at one go

So, what do you prefer for data labeling – an in-house team or outsourcing it to a specialized agency?