Resolving Data Labeling Hurdles With Data Labeling Companies

Data is wealth! With technologies like AI gradually taking over our daily activities, proper data utilization will positively impact society. By efficiently labeling data, machine learning algorithms can provide effective solutions. However, data labeling requires a significant amount of groundwork to be done, like annotations and organizing datasets. Despite the fact that data labeling companies simplify our daily lives, the amount of labor behind it must be understood and appreciated. Let us discuss more about data labeling and the challenges it faces.

An Overview of Data Labeling

Data labeling, also known as data annotation, involves attaching metadata or tags to raw data in order to provide guidance to a machine learning model about the desired target attributes or expected predictions. A label or tag is a detailed component that informs a model about the nature of the individual data piece. Let us understand this with an example.

Assume that a model must predict music genres. Here, the training dataset will include genre labels such as pop, jazz, rock, etc. In this manner, labeled data highlights data characteristics to assist the model in analyzing information and identifying patterns to make accurate predictions.

Data labeling approaches

There are numerous methods for performing data labeling. It is determined by the following: 

1. Quantity of data 
2. Intricacy 
3. Size of the data 
4. Financial and time resources

The different approaches are:

In-house data labeling

Experts within an organization perform in-house data labeling and offer the highest level of precision in labeling. It is the best option when sufficient time, manpower, and financial resources are available. The disadvantage is that it is slow.

Crowdsourcing

There are many specialized platforms for crowdsourcing, where you can register as a requester and delegate labeling tasks to other available contractors. Although cost-effective and relatively quick, this method cannot guarantee high data quality.

Outsourcing

You must understand that outsourcing is a quick means to acquire data labeling services. Data labeling tasks can also be outsourced to freelancers who can be discovered on multiple freelancing and recruitment platforms. But the downside with outsourcing is that you will have to compromise on its quality.

Automatic data labeling

Using active learning, tags can be added to the training dataset automatically. Human specialists have to classify unlabeled and raw data and retrain the model in the event of any failure.

Synthetic data creation

Synthetic data is a replacement for real-world data that is generated artificially. It trains machine learning models and is generated by algorithms. In labeling strategies, synthetic data is an excellent solution when the data is scarce or has diversity issues.

Data Labeling: What’s the Process?

Regardless of the approach, data labeling occurs in the following order.

Collecting data

Any machine learning project must begin with the collection of sufficient raw data. Sources may vary from company to company as a few organizations may accumulate data for years. A few others utilize datasets available. In most cases, this information is inconsistent, corrupt, or unsuitable. Therefore, it must be pre-processed before creating labels. For a model to deliver more accurate results, there should be an adequate quantity of diverse data.

Annotating data

Specialists examine the data and create metadata tags. These may be captions in images that describe the given objects.

Checking quality

Data must be of high quality, dependability, precision, and consistency. The precision with which labels are applied to each data point can assess the quality of training datasets for machine learning models. For this purpose, labelers frequently employ QA algorithms such as:  

1. Cronbach’s alpha test – Measures the average consistency of a set of data items.
2. Consensus algorithm – Reliability of data is accomplished through consent on single information among various systems or individuals.

Testing & training models

Using labeled data to train the model is a logical progression. The procedure entails evaluating the model with an unlabeled data set and determining whether it provides the expected predictions.

Data Labeling Challenges

Any new technological changes or advancements will bring about both benefits and challenges. It holds true for data labeling too. Although data labeling can significantly reduce the time required for scaling a business, it faces a few challenges. They are: 

Increased Cost

Obtaining large amounts of specific data is a challenging task. If a project is managed internally in an organization, the majority of time is devoted to data-related duties such as data collection, preparation, and labeling. Adding tags manually to each article is a time-consuming process. But to effectively manage these tasks so that the work is completed on time, you will need a lot of manpower with experience and skill. Eventually, you will have to spend considerable money on this.

Inconsistency

People with distinct areas of expertise might possess different requirements for labeling. Consequently, the likelihood of inconsistent tagging increases. However, accuracy rates will improve when multiple individuals work on the same data set.

Specialized domain knowledge

You will require labelers with specialized domain knowledge when it comes to specific sectors. For example, creating an ML application for the healthcare sector will be difficult without domain expertise to correctly tag the elements.

Imperfections

Repetitive tasks performed by human beings are vulnerable to mistakes. Regardless of the labeler’s level of expertise, manual tagging may give rise to errors unintentionally. As human beings must work on vast amounts of unprocessed data, it is nearly impossible to ensure that there are no errors.

Wrap up

Data labeling is crucial for machine learning to provide substantial benefits. It is helpful with effective data utilization and supports machine learning algorithms in delivering effective solutions. Although data labeling makes life easy for us, it is essential to understand the challenges and take proper action. With the information mentioned above on the different challenges, you will be able to incorporate data labeling into your workflow in the best possible manner. If your business needs guidance on overcoming these challenges posed by data labeling, contact a renowned company like Opporture in North America, which provides the best AI model training services.