Ensure Accurate AI/ML Outcomes with Professional Data Labeling Services

By now, you must have understood that the ‘best of both worlds’ approach is collaborating with experienced and reputed data labeling companies as they deliver quality results within the stipulated time and budget. It is like a win-win for the companies—getting accurately labeled datasets at scale without compromising data integrity or hygiene.

Businesses globally are implementing new-gen technologies like Artificial Intelligence and Machine Learning to enhance efficiency, increase productivity, and scale new heights. To develop and implement these Machine Learning algorithms, an enormous amount of training data is required—this is where the debate of whether to perform the labeling process in-house or outsource comes in. This article, therefore, helps you figure out why collaborating with experienced AI data labeling companies is a smart move.


Getting the Basics Right

Data annotation is the process of adding tags and labels to the input datasets, which could be in the form of images, text, audio, or video. These tags and labels provide a context for the input and enable the Machine Learning algorithms to learn and interpret the same. They can also calculate the attributes easily.

You want to start an agribusiness, let’s say, and your major goal is to maximize profits by growing good-quality crops. But growing crops abundantly is limited by the number of resources like labor and land. Besides, the quality of crop yields depends on the quality of the inputs used to nurture the plants, such as the environment, the type of seed, and so on.

This scenario is quite analogous to the challenges faced during the development of Artificial Intelligence and Machine Learning models. One cannot reap profits from a business or produce the expected quantity if they are not able to provide the right input. Likewise, stakeholders cannot expect accurate outcomes with small amounts or low-quality of training data.

Challenges in Data Labeling

  • Scale

Getting constant streams of high-quality datasets is a significant undertaking. Popular Machine Learning data annotation applications such as Computer Vision, Deep and Supervised Learning require massive amounts of accurately labeled data. Labeling data at such a huge scale is a time-consuming, resource-intensive, and extremely costly affair.

Moving ahead, feeding the Machine Learning algorithms with a lot of data is not sufficient. Most models require relevant and accurately labeled datasets to be trained properly. They follow the simple GIGO protocol i.e., Garbage in, Garbage Out.

  • Accuracy

The hardest part of building a robust and stable Machine Learning pipeline is adding accurate and appropriate tags and labels. A small case of an incorrectly labeled dataset can nullify the entire process, or even worse, tumble a whole company down. Consider, for example, if patient data is labeled incorrectly and the same is used for developing a new treatment, it might result in government fines, product recalls, and irrevocable reputational damage.

But when the input datasets are labeled correctly, businesses can prevent such scenarios and augment the development of data science and analytical projects that can drive sales, deliver market insights, and optimize company costs.

In-house V/s Data Labeling Services

For the majority of companies, getting an in-house data labeling setup is the holy grail they can rely on. It can offer them advantages such as better protection for their IP, data security and integrity, direct oversight, and so on. But, the process of developing the enhanced quality training datasets necessary to build AI models is often complicated and prohibitively expensive. Instead, collaborating with reliable data labeling companies is a comparatively smarter option.

Here’s a list of benefits that businesses gain by investing in outsourced data annotation services:

  • Cost Efficient

Not many organizations can redirect the necessary time and resources needed to recruit, train, and manage a team of data labelers. Do not forget the extra office space required and the need to develop the right software and tools. As a result, costs can swiftly spiral. Whereas, the professionals already have the potential required for data labeling tasks. They use the best-fit tools to annotate datasets and assure excellence.

  • Agility

Labeling large volumes of data can monopolize your employees’ time; as a result, they might not be able to give enough attention to this pivotal task. They may or may not be able to handle the spikes in the volumes of input datasets. Instead, the service providers are well-trained and can efficiently label large volumes of data in time to meet a project deadline. Besides, they offer the agility to manage requests such as adding different types of data.

  • Quality with Accuracy

Partnering with an established service provider can help businesses save money without sacrificing quality. In any professional data labeling company, they employ trained, professional annotators who can quickly adapt to any demand at any given time. They are also familiar with the most up-to-date and sophisticated annotation tools as well as have various quick techniques rolled up their sleeves.

  • Data Integrity

Data safety and security are of the utmost importance for companies. In fact, the majority of stakeholders are reluctant to outsource their data labeling projects due to this reason. They have their apprehensions regarding privacy compliance such as PHI and PII as well as other similar considerations. Fret not as the professional providers incorporate widely accepted guidelines on ethics and integrity.

Bottom Line

By now, you must have understood that the ‘best of both worlds’ approach is collaborating with experienced and reputed data labeling companies as they deliver quality results within the stipulated time and budget. It is like a win-win for the companies—getting accurately labeled datasets at scale without compromising data integrity or hygiene.

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