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4 Distinct Approaches to Supercharge AI Models with Automated Data Labeling Process

In the highly dynamic fields of artificial intelligence (AI) and machine learning (ML) solution development, highly automated and efficient project workflows are essential for the success of any ML solution. The end goal is to achieve the required metric values quickly with optimum resources in the loop. Among the different stages involved in the machine learning pipelines, it is the AI data labeling and quality assurance that developers consider as most resource-intensive and time consuming.

data labeling services

This is why automation of these processes are now considered as a pivotal point that development teams must focus on while designing the project workflows. By adding automation into this mix, the entire development project can be streamlined while improving RoI gains significantly.

By adopting automated data labeling services, business can:

  • Free up resources and valuable time which can be further directed toward other value-generating tasks.
  • Minimize inconsistencies and errors in data, leading to enhanced accuracy and improved results.
  • Create a highly effective, efficient, and scalable ML-pipeline which delivers augmented results in lesser time.

Understanding Automated Data Labeling & Its Many Nuances

Automated data labeling can be defined as a process involving utilization of machine learning algorithms, simple heuristics, and different labeling automation tools and techniques to automate or semi-automate the create of annotation for datasets. The primary goal of automated data labeling is to improve accuracy and efficiency with the strategic adoption of human-in-the-loop (HITL) approach to make a greater impact. The fundamental steps of utilizing automated data labeling techniques are as follows:

  • Collection of the raw data that needs to be annotated.
  • Selection of the accurate automated data annotation and labeling technique. If a technique requires training of a model on a limited batch of labeled data, it needs manual efforts to annotate a specific batch to prepare the required model.
  • Initiate the annotation utilizing the specific technique or trained data model.

Through automated data annotation and labeling, teams can seamlessly label huge volumes of data which creating accurate labels within second to accelerate the project development and optimize productivity.

The 4 Different Approaches to Automated Data Labeling

One of the many ways of perceiving automated data annotation is to understand the four distinctive categories or complementary layers, each packed with unique feature. With an in-depth understanding of these perspectives, one can understand data annotation in a better way and select the right approach to improve the labeling process.

Layer 1: Pre-labeling vs. HITL

The first layer involves the degree of human efforts needed for this approach to work efficiently. Pre-labeling involves end-to-end automation of the labeling process with no human intervention. On the other end, HITL involves human intervention to review and correct the automated labels.

  • Pre-Labeling: Pre-labeling is a cost-effective and efficient way to annotate huge data volumes without the need to integrate the model with a labeling tooling pipeline. Annotations can be generated and reviewed by human annotators for quality. This approach is better for use cases which are easy to label and uncomplicated.
  • Human-in-the-Loop (HITL): The HITL approach combines both manual annotations and pre-labeling and can help improve the quality and accuracy of labels, especially where data is ambiguous and complex. For edge cases that usually automated techniques can’t handle alone, HITL approach is the right pick.

Layer 2: Pre-trained Models vs. Custom Models

With the increasing popularity of automated data labeling techniques, AI models play a significant role in the entire process. Here the machine learning (ML) team need to pick from a custom model and pre-trained model as the choice will significantly impact the effectiveness and accuracy of labeling technique.

  • Pre-trained Models: There are pre-trained ML models which have been extensively trained on massive data volumes and can be used for natural language processing, object detection, or image recognition. Being easily available, projects with limited resources can be easily started. These models require little expertise, are easy to use, and optimized for business-specific ML tasks, so that businesses cover as many as use case as possible.
  • Custom Models: Opting for a custom model is a better approach when pre-trained model isn’t the right fit. This approach involves developing an AI model in accordance to the business needs. The developed AI model is trained on unique datasets that are relevant to the business specific use cases. In this manner, the model can be optimized to meet the business needs with improved performance and accuracy while extending more customization and flexibility.

Layer 3: Hosting vs. Hosted Model

Automated labeling comes with the third layer or perspective that involves the selection between hosting an AI model or using a hosted model, further brainstorming on who would be responsible for hosting.

  • Hosting: Hosting an in-house AI models involves its infrastructure as well, providing the in-house team complete control over security and privacy without any requirements of ecosystem lock-in or customization. However, technical expertise and adequate resources are required to maintain the AI model hosting, which can be time-consuming and expensive.
  • Hosted: In the hosted model approach, a third-party, usually data labeling companies, host the either pre-trained model or the developed custom model for further maintenance. There are almost minimum obstacles pertaining to the set-up, use, and deployment of the model. These services are flexible and scalable, are offered as a service, and pose no infrastructure issues, making it the most cost-effective and viable option.

Layer 4: State-of-the-Art (SOTA) Vs. Proprietary Tech

During the solution development, data labeling outsourcing form must select between proprietary tech or replicate the state-of-the-art technology as the final layer. It is the most important decision, thus requires careful consideration from all aspects.

  • State-of-the-Art Tech: When AI-model is hosted in-house, there’s complete control over security and privacy. Through SOTA approach, there’s a complete replication of the best technology available publicly for the utilization. This is one of the best ways to achieve optimum automation as this facilitates the utilization of the most advanced, modern, and efficient to deal with the task at hand. This provides a huge advantage over other teams opting for relatively lesser modern tech.
  • Proprietary Tech: The proprietary tech approach involves development of in-house machine learning solutions without the aid of SOTA. Through this approach, the intended solution fits the specific task like a glove without any rules limiting any future customizations. Developing such proprietary tech involves lesser capital investments and technical expertise when compared to utilization of SOTA. However, this approach is only feasible for an uncharted field or a small niche.

The Bottom Line

Though two options of each approach are outlined here, the beat approach is to use the combination of both to optimize the data labeling process. For the first year, the combination of HITL and prelabeling technique should be used. In the second year, pre-trained models can be used in early annotation, which can be later fine-tuned till it becomes the custom model. For the third layer, we advised using the hosted model approach as it reduces the risk associated with data security by adhering to GDPR regulations and maintaining HIPAA compliance. For the final layer, most of the data labeling services providers recommend the SOTA approach as it provides clients with most effective way to address the automation challenges, giving the ML development team a much-needed edge over others without investing too much time, money, and resources.


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