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Do you know which thing has the greatest value in terms of money at the current time? Guess it with this hint, the thing is not a material object but rather an intangible thing. Yes, you guessed it right, it is data, which is valuable more than money. But wait, can you sell your data to any company of your choice and become a billionaire overnight? Simply no! The process of data collection is complex and utilizing the processed data to earn money is furthermore complex.

Companies use automated data labeling processes to collect and process their data in order. Companies process huge chunks of data regularly to draw conclusions from it, which also helps them to make the right decisions. They collect and process the data using the computer system to get their work done quickly.

The development of Artificial Intelligence (AI) largely depends on the availability of labeling data. The more data the system would have the more precise result it would generate. Processed data is a must for the development of AI and that’s why developer companies are focusing more on automated data labeling processes.

This blog aims to justify the automation of labeling process by explaining its benefits along with its algorithms.

A. What is Data Labeling?

Companies collect data from the virtual market in raw format, which typically includes customers’ information. Companies spend huge sums of money in assessing this data to get valuable insights from it. The procedure they take to organize the data is called labeling data. Earlier, the process used to be done manually but at the current time it is done via the use of Artificial Intelligence.

Human beings can make conclusions from the raw data but computers cannot unless taught with the amalgamation of AI and Machine Learning, automated data labeling processes getting simplified for the understanding of computers. Therefore, software programs can process data as per the instructions and help in analyzing the raw data.

The task of labeling data includes multiple things included such as text classification, image recognition, etc. Nowadays, all these tasks of labeling are done via the automated process, which ensures faster and error-free labeling.

B. Explaining Automation of Data Labeling

With the coming of Machine Learning (ML) algorithms, human labeling of data completely vanished. The way businesses used to process large volumes of data has been revolutionized over the last few years. They now use ML algorithms to process any quantity of data, which has automated the data processing system.

The integration of automated labeling algorithms involves the creation of small sets of labeled data to train a model. At the initial stage, the training model only segregates labeled data and unlabeled data. With time, the model improves as it takes more data to process and achieves an optimal level of accuracy. Every big organization has its separate data labeling model that helps to process the data of its dedicated customer group.

The process of labeling data automatically is faster than manual labeling and it includes a wide range of applications. Speech recognition, sentiment analysis, image recognition, etc are part of the process. All these classifications of data have different sights and companies use their sights to make futuristic decisions.

For instance, in image recognition, automated data labeling is used to identify different objects in the image. The objects can be people, animals, trees, roads, or any other things but all the objects are getting recognized to help computers to know these objects. Interestingly, sentiment analysis is used to assess social media posts more deeply and gather people’s opinions.

C. Benefits of Automated Data Labening

Just like the automation benefits, the automated data labeling process can provide many advantages to companies that assess huge quantities of data. Some of the main benefits of auto-labeling are explained here in brief.

Accuracy Enhancement

When you choose automation in labeling data you will automatically choose to eliminate human error. With auto-labeling, the system will only generate consistent and accurate results. The ML model you have created for the data processing task will increase its potential. With automation, the quality of your ML model will increase and the risk of biased results will become minimized.

Handling Large Datasets

When you choose the automation of data labeling process you automatically choose to handle a huge quantity of datasets to process. Big companies handle huge datasets regularly to process accurate results. With automation, they can handle all these tasks within less time than manual processing. Your ML model would get training to handle large data when you process more data regularly.

Handling large datasets via automated data labeling processes allows the ML models to process a variety of datasets. Your ML model would get trained to handle to feed any amount of data to the database and it would not be affected by the workload at all. The model can automatically fix issues that would occur in the system while processing huge quantities of data altogether.

Elimination of Human Errors

Which process would have more chances to produce error-free data? Auto-labeling or human labeling? Obviously auto labeling! Labeling the datasets by humans can leave errors in the processing but with automation there is no chance of error. Human beings make errors in the database files due to inconsistent workflow, biases, workload pressure, or other reasons. But with automation, there is a zero chance of occurring any mistake in the file.

Cost-effective

Besides being time-consuming, manual labeling of data is expensive too. This also has the chance to have some errors in the files due to subjectivity. However, in the automated data labeling process, you can get the data labeled on time and it would not cost you more at all. Auto-labeling would reduce the need for manual work in the data labeling process to a very high degree. That’s why ML models are more cost-effective when it comes to data labeling tasks.

Faster and Efficient

Without human intervention, auto-labeling can label a large quantity of data in a fraction of a second. Besides the speed, the process would generate accurate results after the data assessment process. This allows ML projects to shoot their growth within a few years by labeling data with automation. Self-driving cars are one of the best examples of efficient data labeling, which has helped ML projects to grow more in the future.

Enhanced Workflow Productivity

Nowadays, companies are using ML or AI to label their data for further assessment. The development of AI projects and ML projects is largely dependent on data labeling tasks. With automated data labeling, the task of data labeling becomes super fast. So data scientists now can think more about AI development projects rather than focusing on other tasks. Overall, automation has increased the productivity and gave a momentum to the workflow.

D. Automate Vs Manual Labeling

The comparison between automated and manual data labeling has already been debunked in brief. However, there are a few things that still need to be added to get a complete picture of the data labeling process.

The use of a machine learning algorithm is the main distinguishing area that separates manual labeling from automated labeling. Processing of synthetic data is also possible through the automated data labeling process. Processing of synthetic data cannot be done manually as it is a complex process, which has multiple layers.

However, on the other hand, when it comes to handling critical data or subjective data manually labeled data is the best. Human reviewers can eliminate small errors from the data and label the data appropriately, which cannot be done easily by any automated ML algorithm. However, labeling data at a large scale requires the help of machine learning to eliminate errors.

Deciding whether you need manual or automated data labeling depends upon the project. You can choose human data labeling for some projects where the size of the dataset is small and the data is very subjective. On the other hand, auto-labeling is suitable for those projects where the size of the dataset is large and the data is objective. Therefore, you need to keep the project type in mind before choosing any method for labeling the data.

E. Understanding Techniques of Automated Labeling Algorithms

As pointed out before, Machine Learning and Artificial Intelligence are important aspects of the automation of labeling process. Various types of methods are there to label datasets including deep learning, supervised learning, unsupervised learning, etc. Some of the approaches to auto-labeling are detailed here.

Deep Learning

In Machine Learning, multiple subsets work together to label data and deep learning represents one of such subsets. This technique builds a neural network for labeling data, which is composed of interconnected nodes. This network has multiple layers and each layer performs a specific task. This technique is helping in building AI image recognition models such as driverless cars through its outstanding image extraction and processing capacity.

Supervised Learning

Supervised learning is the commonly used automated data labeling process where ML models are learning the data labeling process by observing the existing labeled datasets. This technique is used in various AI application processes such as image recognition, speech recognition, etc. In this technique, ML models or algorithms minimize the errors in the predicted output by learning processed datasets.

Deep Learning

In Machine Learning, multiple subsets work together to label data and deep learning represents one of such subsets. This technique builds a neural network for labeling data, which is composed of interconnected nodes. This network has multiple layers and each layer performs a specific task. This technique is helping in building AI image recognition models such as driverless cars through its outstanding image extraction and processing capacity.

Unsupervised Learning

Unlike supervised learning, ML models or automated labeling algorithms identify data patterns without any supervision. This method uses group data points or clustered algorithms to identify the patterns to label datasets. This method is commonly used in AI applications to assess customer segmentation, anomaly detection, etc.

The need for automated data labeling is growing with time as companies are processing more data for their betterment. Auto-labeling offers fast data processing and reduces time than other data labeling processes. The techniques of auto-labeling are attractive and better for developing Artificial Intelligence and other ML algorithms.

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