Importance of Data Labeling Services

Data labeling services powered by AI platforms can help to train custom ML models by utilizing qualified data labeled by a trained workforce. They label different types of digital data like audio files, text, images, videos, products, services, search queries, and more. Once the data is labeled, it’s used for training advanced AI algorithms to recognize the patterns in the future for identifying similar data sets

Artificial intelligence (AI) is a powerful platform if the training quality is maintained. The accuracy of training data will impact the quality and success rate of the AI model, around 80% of the efforts on an AI model is data wrangling, with the help of data labeling. While constructing an AI model, we must work with a large quantity of unorganized data. Labeling those raw data is the key process in organizing the data and preprocessing for developing accurate and quality AI algorithms. In short data labeling in Machine Learning (ML) refers to the procedure of identifying and labeling data samples, that's crucial in the case of data-driven learning. Sophisticated learning happens when both inputs and outputs data are categorized to enhance the upcoming learning of an AI model. The complete data labeling workflow comprises data annotation data annotation, classification, tagging, moderation, and processing. We must maintain an end-to-end and structured process to convert raw data into important training data to feed AI models, which patterns to detect and allow for generating the expected results. For instance, if we are training data for a handwriting recognition model, we may need an OCR device to detect the character and tag them accordingly like the language used, which category it may fall into, what type of message it is, etc. If a model is required to voice recognition, we need to label the data files with different tagging like the recognized language, message content, vocal patterns, tone, gender, age, etc.

Types of Data Annotations

Data labeling requires high accuracy to train AI models to take exact predictions for future AI algorithm recognition. It involves multiple processes to maintain an exceptional and perfect data model. We offer different types of data annotations, they are as follows:

  1. Categorization
  2. Bounding Box
  3. Semantic Segmentation
  4. Line Annotation
  5. Cuboid Annotation
  6. Key Point Annotation
  7. Contour Annotation
  8. Poly Mask Annotation
  9. Polygonal Annotation

Data Labeling Techniques

It’s crucial to opt for the proper data labeling service for your company, as this decision involves the efficient utilization of available resources and time management. To accomplish data labeling majorly we have 4 ways, as listed below:

  • Crowdsourcing: It is an ideal option to crowdsource your data labeling requirement with an authorized golden partner if you are lacking in-house resources. An authorized data partner can demonstrate their expertise during the AI model build technique and distribute data to a massive crowd of contributors who can deal with huge data faster. Crowdsourcing is good for organizations that expect ramping up toward huge-scale deployments.
  • Outsourcing: If your requirement is temporary and have little data to label, hiring freelancers can fulfill your need. You may need to evaluate the skills of these freelancers by giving the sample work initially, however, you will not have complete authority over the workflow management.
  • Automation: Data labeling also can be accomplished by the automation device. If you are looking to create training data at scale and flawless, data labeling powered by machine learning is an ideal choice. It can also be used for automating enterprise tasks that need the continuous classification of information.
  • Managed Resources:

    It is recommended to make use of managed resources, where you are looking to label/annotate the complex data. In this approach, we will train these resources as per your requirements or guidelines, to ensure the quality data is maintained. These resources will be under additional supervision for enhanced outcomes.

    The technique your company chooses will rely on the complexity of the hassle you’re aiming to resolve, the capability of your resources, and your financial constraints.

Importance of Quality Assurance

Quality Assurance (QA) is a frequently neglected but critical factor of data labeling services. Ensure to maintain high standards of annotations in case you’re dealing with data processing internally. If you have outsourced your data processing for crowdsourcing, they’ll follow the QA technique to ensure quality. Quality Assurance is a vital factor because data labeling should fulfill different traits. The labeled data to be unique, meaningful, and independent. The assurance of data labeling accuracy is highly recommended to generate quality. For instance, while labeling images for health care, all details like the patient's name, age, problem, diagnostic reports, and medicines are to be accurately labeled to prescribe suitable medicines to heal the health issue.

Training and Testing of models

When you've got labeled data for training, we need to get it reviewed by the QA team, if it has cleared the QA, we can ensure that the quality data is in place. This qualified data is now ready for training the AI models. Now we can use the qualified data to test a new set of unlabeled data to analyze the results of predictions. You’ll have various predictions on accuracy based on the requirements of your training model. If your model is processing diagnostic reports to figure out the health condition, there should be a higher level of efficiency and accuracy when compared to discovering gadgets in an e-commerce store, as life is dependent on report analysis. So, you need to set your goals based on the requirements and models you choose.

Take advantage of HITL

When trying out your data, human intelligence must be incorporated to monitor and detect the data with a closer look. Making use of human-in-the-loop (HITL) helps you to verify that your model is making valid predictions, trace gaps in training data, provide responses to the model, and keep it handy when low-quality or inaccurate predictions are taken.


Adopt dynamic data labeling techniques that support you to enhance performance. Make sure to repeat these procedures as and when they are required.

Quadrant Resource's Data Labeling Expertise

At Quadrant Resource, our crew of professionals makes us stand out as the high-quality feasible data annotation platform in the crowd. The crowd workforce contribution to our Data Annotation Platform helps us to excel in industry standards in ensuring quality data labeling services. Our top 3 observations on data labeling are as follows:

  1. The team’s success relies on shortlisting and prioritizing use cases, target personas, and performance metrics. This facilitates identifying training data requirements, making high availability models for matching any type of situation, and mitigating probable bias due to the shortage of different datasets. Additionally incorporating a diverse pool of crowd workforce for data labeling can help keep away any bias encountered while labeling.
  2. Data drift is very abnormal than you may imagine. In the current digital era, the data that your model visualize is very changing at a great pace, and the model which you trained past week might not produce the quality that meets your prediction. So, it’s critical to develop a scalable, automated training data pipeline to consistently train your model with new data.
  3. The primary concerns are Security and privacy, to be handled ahead of but not as a post process. Frequently monitor and eliminate confidential information that is not required for training the model to be most reliable. Choose a safe and corporate class data labeling platform for training confidential data labeling projects with secured information. Select an expert crowd workforce who is trained to deal with sensitive information.

How We Can Help You

We offer data labeling services to enhance AI and ML at scale. As we are the leader in data niche worldwide, our customers exploit our caliber to deliver large volumes of accurate data faster in almost every format, such as Search, picture, audio, text, and video to fulfill your desired AI program requirements. To explore how we can assure accurate data labeling services that build trust to implement AI. Drop an email with your requirements to get in touch with our Specialist.