What comes to your mind when you think of artificial intelligence, machine learning, and deep learning? A generalized notion of how we perceive AI is movielike robots that outperform human intelligence. Some of us also think of AI as machines that consume data and learn by themselves. Apart from this, we also consider intelligent virtual agents, deep learning, neural networks, machine learning, predictive analytics, etc., as a other names for AI.

Whereas, Artificial Intelligence is the capability of a machine to learn from data and evolve. Machine learning algorithms have very limited capabilities and cannot learn without human guidance data labeling is, therefore, the way by which computer systems become a smart. This piece provides detailed information about the data labeling process, the challenges associated, and why you must collaborate with data labeling companies.

Understanding the Process

Data labeling is the process of adding tags to raw data to help the machine learning models easily calculate the attributes. These tags show the smart model the target attributes or answers, it is expected to predict. A tag or a label is the descriptive element that helps the machine learning algorithm learn by example.

Take, for example, the smart model is supposed to predict music genres. So, the training datasets here will be composed of a variety of songs with labels representing genres such as classical, jazz, pop, rock, and so on.

Know the Challenges

Though data labeling is not a rocket to be launched in the sky, yet is a significant undertaking. Errors or inaccuracies while adding labels deviate from the outcomes. Mentioned here are some of the significant challenges associated with the process:

Time Intensive

Getting massive volumes of data (specifically for highly specialized niches like healthcare) is not only a tough challenge, but also a resource-intensive task. The time-consuming nature of the data labeling process makes it extremely difficult for human labelers to manually add tags to the input data sets. Data preparation takes up around 80% of the project time within the full cycle of ML development.

Prone to Errors

The manual tagging and labeling process is prone to human errors; no matter how experienced or attentive the annotators are. It is because the human labelers have to deal with massive volumes of raw data—a person marking 150,000 pictures with up to 10-15 objects each. Just imagine!

Inconsistencies

As different annotators have different levels of expertise, labeling criteria and descriptions themselves might be inconsistent, thus adding on to the challenges list. Besides, there can be disagreement on labels between annotators. For example, one reviewer might take a hotel review as sarcasm and add a negative label; whereas another expert may score it positive.

Taking the Right Approach

There are numerous approaches to the data labeling process. The style you chose varies according to the amount of data to be labeled, the complexity of the problem statement, and the size of the data science team. And how can you miss your financial resources and available time? Let me take you through the different approaches quickly:

Crowdsourcing

There are dedicated crowdsourcing platforms like Amazon Mechanical Turk. These platforms need you to sign in as a requester and assign the labeling tasks to contractors available there. It is a comparatively affordable and relatively faster approach but doesn’t guarantee the quality of the labeled data.

In-house

In-house data labeling is performed by a competent pool of professionals within the company. Usually, it is considered as the holy grail; however, it is not the only solution! You can opt for this option when you have adequate resources in terms of time, money, and trained personnel. Though it offers the highest labeling accuracy possible, it is slow on the flip side.

Outsourcing

Outsourcing is just another and perhaps the most efficient way to get things done within the stipulated time and budget. You can either consult a professional or collaborate with accomplished data labeling companies to outsource such core tasks. They have the potential required to perform the labeling process accurately; thereby, ensuring excellence in every outcome.

AI Data Labeling

Yes, you get that right! The data labeling process can also be assisted by software. Labels and tags can be added automatically via the active learning technique. Putting it simply, human annotators create an AI auto-label model that scripts the raw data. The outputs are then verified. If the model fails to accurately label the datasets, human-in-loop labelers make the corrections and further re-train the model.

Further Takeaways

As a decision-maker, you must leverage an approach that helps you in attaining profits across different aspects. Hence, collaborating with data labeling companies is the most trustworthy solution to optimize operational costs without impacting the quality of outcomes they work as an extended in-house team to help you with AI data labeling and expedite the smart model implementation. Isn’t this a brownie point?

Source: DataFloq