Data annotation is the process of labeling and tagging various types of data—such as images, videos, text, and audio—so that AI/ML models can interpret and learn patterns using different machine learning algorithms. This process plays a crucial role in building high-quality datasets for AI models.
In AI/ML model development, workflows are interconnected: each workflow's output serves as the input for the next. The quality of the final product—fully functional AI models—depends on the quality of each step in the pipeline. The quality of output data cannot exceed the quality of input data, a principle commonly referred to as garbage in, garbage out.
Typically, the first two processes in the pipeline are data collection and data preparation, which involve gathering, cleaning, standardizing, and annotating data. Data cleaning removes duplicates, null values, and sometimes outliers. Standardization ensures consistency in format and values. While these steps follow well-established practices, data annotation remains an evolving field, as evidenced by the growing number of image annotation solutions, image labeling tools, and data annotation services.
Shohrukh Bekmirzaev
Data Annotation Tools Review | Unitlab Annotate
One key question remains: should data annotation be outsourced? For many companies developing AI models, the decision to manage annotation in-house or outsource it to a third party is critical. While outsourcing often provides cost and time efficiencies, it also raises concerns regarding quality control, security, and long-term dependency.
Thus, we decided to examine this question in depth. By the end of this discussion, we aim to answer these key questions:
- What is data annotation outsourcing?
- What are its advantages and disadvantages?
- What conclusions can be drawn from this analysis?
What is Outsourcing?
In AI/ML model development, the general 10 times rule applies: you need at least ten times more data points than the parameters (degrees of freedom) in your model. For instance, if a vehicle detection system has 1,000 parameters, at least 10,000 labeled data points would be required for effective training. For complex AI models, such as NLP and generative AI, this requirement increases exponentially.
The exact data requirements vary based on multiple factors, but a diverse and extensive dataset generally leads to more effective models. This presents a challenge: how do we annotate such vast quantities of data efficiently? One approach is AI-powered auto-labeling, yet human oversight remains necessary to ensure accuracy. The emerging standard is hybrid annotation, which combines automated tools with human review.
Hojiakbar Barotov
Hybrid Annotation | Unitlab Annotate
But whose expertise should be relied upon? There are two options: hiring and training an in-house data annotation team or engaging a third-party data labeling service. Outsourcing refers to hiring external data annotators—typically via a data annotation platform—to label datasets.
By outsourcing, businesses pay for annotation services and receive the labeled dataset without the complexities of managing, hiring, or training an internal team, significantly reducing overhead. Outsourcing also enables AI/ML companies to scale their annotation tasks efficiently, access expert annotators, and focus on core model development. However, is outsourcing always the best solution? Let’s explore further.
Advantages of Outsourcing Data Annotation
Below is an infographic summarizing the pros and cons of outsourcing. Let’s explore each point in detail.
1. Cost Savings
Building, training, and maintaining an internal data annotation team requires substantial investment. Beyond salaries and benefits, infrastructure and operational costs accumulate. Additionally, once the initial annotation phase is complete, the team may experience a lull in workload. Should they be retained or dismissed and rehired later?
Outsourcing follows a contract-based model. A team is hired for a project or a fixed period, and once the contract concludes, both parties move forward. If needed, the contract can be renewed without concerns about idle employees, ongoing training, or employee benefits. This pay-as-you-go model makes data labeling cost-effective, particularly for startups, small AI teams, and one-off projects.
The exact price of the project depends on multiple factors, such as the vendor, data annotation type, labels, and so on. For example, in the case of a simple vehicle detection system with 10,000 images that need bounding boxes and classfication with 5 labels per image, the estimated cost would be around $3,500 according to the Mindkosh. For a one-off project, this is more economical than starting from scratch.
2. Time-saving
Establishing an in-house labeling team not only demands financial resources but also requires a significant time commitment. The recruitment and training processes delay the annotation workflow.
By contrast, outsourcing eliminates these overheads. The only time required is for the actual annotation process itself. Additionally, external annotators—who specialize in large-scale data labeling—often complete tasks more efficiently than in-house teams.
3. Access to Expert Annotators
Outsourcing allows businesses to specify conditions requiring annotators with particular domain expertise. While this may increase costs, it ensures high-quality image annotation, which is particularly valuable in specialized fields such as medical imaging or legal document processing.
Moreover, external annotators are already proficient in their data labeling tools, enhancing efficiency and accuracy in annotating datasets.
4. Scalability
AI/ML model development demands vast amounts of data. To fine-tune models, additional datasets are required over time. By outsourcing, multiple teams can work on dataset management simultaneously, accelerating the process.
When workloads decrease, outsourcing can be paused, eliminating unnecessary costs associated with maintaining an idle in-house team. This flexibility allows businesses to dynamically scale their data annotation efforts based on current needs.
5. Focus on Core Operations
For most AI/ML models, data collection and annotation are the fundamental steps that more or less do not change. All AI models need high-quality, diverse, standard datasets for high accuracy and usability. While a poor dataset hinders the quality of an AI model, a high dataset does not always guarantee a high-quality, practical, and useful model.
While data annotation is essential for AI model development, it does not define a model’s uniqueness. Research, experimentation, training, and optimization are more critical. By outsourcing annotation, AI teams can allocate more resources to core tasks such as model development, deployment, and business strategy.
Disadvantages of Outsourcing
Despite its benefits, outsourcing the data labeling workflow comes with challenges that must be carefully evaluated. These problems below are more like a checklist: you need to review it before making a contract with a data annotation outsourcing service.
1. Data Security and Regulations
Unless you are building an open-source AI/ML model with open training data, your source data are usually proprietary. If training data is proprietary, outsourcing increases the risk of data breaches. While internal teams also pose security risks, outsourcing compounds them as contractors operate outside direct oversight.
Additionally, outsourcing necessitates compliance with regulations such as GDPR (EU) and HIPAA (US). Ensuring that third-party providers adhere to strict security protocols is crucial.
2. Vendor Lock-in
The cost and time advantages of outsourcing data annotation primarily benefit small companies or those working on one-off projects. The idea is that management does not need to invest in infrastructure or training for an in-house team. However, this model is less effective for organizations that regularly develop AI/ML models.
In the short term, outsourcing may seem cost-effective. However, for companies that continuously build and refine AI models, investing in an in-house team can be more economical. Regularly training and improving annotation pipelines allows businesses to distribute costs over time, keeping long-term expenses lower.
Additionally, vendor lock-in is a major concern. As companies continue outsourcing, the prospect of assembling and training an in-house data labeling team becomes less appealing. Over time, dependency on external providers increases, reducing flexibility and potentially leading to higher costs as vendors adjust their pricing or terms. What begins as a cost-saving strategy can evolve into a restrictive long-term commitment, limiting future options.
Long-term reliance on external annotation providers creates dependency risks. If a vendor raises prices, modifies services, or ceases operations, transitioning to another provider or bringing annotation in-house can be difficult and expensive.
3. Operational Concerns
Even when outsourcing, ensuring high-quality, consistent data annotation remains crucial. Does the service provider have mechanisms to monitor annotation quality? If the final dataset does not meet expectations, what recourse is available?
Effective communication between your team and the outsourcing partner is also essential. Misalignment on guidelines, time zone differences, and language barriers can result in inconsistencies and errors, complicating the workflow.
Conclusion
To outsource data labeling or not? That’s the question.
Like most technical decisions, it depends. The key considerations are:
- Is data annotation a consistent part of your operations or a one-time task?
- Do you have the budget and resources to build an in-house annotation team?
Outsourcing offers benefits such as cost savings, scalability, and access to expertise. However, potential risks include data security concerns, vendor lock-in, and quality inconsistencies. Ultimately, the decision should be based on the specific needs and circumstances of your business.
Explore More
- 11 Factors in Choosing Image Annotation Tools
- Four Essential Aspects of Data Annotation
- Importance of Clear Guidelines in Image Labeling
References
- Cogito Tech. (Jul 3, 2023). Outsourcing Data Labeling Tasks – How to Go About? Cogito Tech: https://www.cogitotech.com/blog/outsource-data-labeling-tasks/
- Sumit Singh. (Jan 4, 2023). Data annotation outsourcing: A step-by-step guide. Labellerr Blog: https://www.labellerr.com/blog/data-annotation-outsourcing-a-step-by-step-guide/
