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Data Annotation Service

Tough Choice: Should You Tackle Data Annotation In-House or Outsource Data Labeling Work

Should you label your data by yourself or outsource the work to a data labeling service provider? How to find a reliable vendor?




Admon W.

Find the Best Way to Get Your Model Ready for the Market Early

Artificial intelligence (AI) is rapidly advancing and has a significant impact on a variety of industries. Computer vision is one of the areas where AI is having a significant impact. The AI in computer vision market size is expected to reach $51.3 billion by 2026, at a CAGR of 40.1% during the forecast period from 2021. The rise of vision AI has been nothing short of extraordinary, transforming industries by automating processes and providing new insights at a breakneck pace. At the heart of these groundbreaking advancements lies the critical process of data labeling.

Get Training Data by Outsourcing Data Labeling Work

However, in a typical AI project, professionals may face several obstacles when tackling data labeling, including:

Time-intensive process: A Cognilytica research report reveals that sourcing, cleansing, and managing data account for over 80% of the time spent on most AI and Machine Learning projects. To propel AI and ML advancements and ensure individual projects deliver their promised benefits, organizations must find effective ways to overcome data labeling bottlenecks.

High costs for annotation tools: Many companies turn to external data annotation platforms, which can result in significant procurement expenses. Some organizations attempt to build their own tools or explore open-source alternatives. The former requires additional human resources, while the latter often lacks support and may be limited in functionality or data type compatibility.

Skilled workforce allocation: Allocating highly skilled data scientists and AI professionals to data labeling tasks can lead to inflated costs, while opting for inexpensive, inexperienced annotators may compromise label quality. Striking the right balance between cost and expertise in the labeling team is vital for successful projects.

Neglecting quality assurance: As Dzone highlights, incorporating quality checks can add significant value to data labeling processes, particularly during iterative stages of machine learning model testing and validation. Implementing a robust QA process can significantly enhance the quality of labeled data, ensuring better model performance.

Subpar data label quality: Several elements can impact the quality of data labels, with three crucial determinants being people, processes, and tools. Ensuring that annotators possess domain knowledge and follow well-defined guidelines can help maintain label consistency. Additionally, using advanced annotation tools and continuously refining workflows can contribute to higher-quality data labels.

Scaling limitations: Scaling data labeling operations is essential when dealing with growing volumes and expanding business or project needs. However, organizations that handle data labeling in-house often struggle to scale their tasks accordingly. Implementing strategies for flexible scaling, such as leveraging cloud-based annotation tools or outsourcing to specialized data labeling firms, can address this challenge.

Achieving a swift market launch is the goal of all AI projects, with quality and efficiency being two crucial factors to consider. Since open-source datasets often fail to meet the diverse and specific requirements for different projects, AI/ML engineers need their own training data sets. In preparing the training data, there are two common approaches: in-house data labeling and outsourcing data labeling.

Data Annotation: In-House vs. Outsourcing

In-house data labeling uses the company’s own data scientists and facilities for data labeling. Outsourcing data labeling is a process in which AI / ML engineers outsource the data labeling work in their project to a third-party annotation vendor. Should you label your data by yourself or outsource the work to a vendor? Let's compare the two options so you can have a clear decision.

Labeling In-House vs. Outsourcing Data Labeling

⏰ Time Requirements

In-house data labeling can be significantly more time-consuming due to the need to train the team on methods, tools, and processes. Outsourcing saves time as it eliminates the need for companies to train their teams and build the necessary infrastructure.

💰 Cost

In-house data labeling can be expensive, as it requires building infrastructure and training employees. Outsourcing is generally more cost-effective since it reduces investments in hardware and the need to hire dedicated data scientists for labeling tasks.

✅ Quality

Comparing quality between in-house and outsourcing alternatives can be challenging, as data labeling service providers may have different areas of expertise. However, companies can find data labeling vendors that offer high-quality services to suit their needs.

🏭 Scalability and Flexibility

For simple projects without specific requirements, an in-house data labeling team might suffice. However, if your project has unique, complex needs, outsourcing your data labeling tasks is recommended for greater scalability and flexibility.

🛡️ Security

In-house data labeling ensures that data is not shared with third parties, making it the most secure strategy. Outsourcing to companies with necessary certifications or qualifications can also provide a high level of security while ensuring that your data is well-protected.

🔧 Tools

Data labeling tools can impact cost, efficiency, and training data quality. In-house data labeling may necessitate building a custom platform or purchasing a data labeling platform. Not all outsourcing data labeling teams have their own platforms, so finding a professional data labeling vendor can help keep costs down while delivering the desired results.

When to Keep Data Labeling In-House: Key Considerations

In some limited situations, in-house data labeling may be the more suitable choice. For example, consider keeping data annotation in-house when your project's data volume is manageable and your team has the capacity to handle it efficiently. Another reason to opt for in-house labeling is when the project involves sensitive information or intellectual property that should be restricted to company employees. Additionally, if your project has unique requirements that only internal resources or proprietary tools can address effectively, in-house labeling might be the better option. Finally, if onboarding and training external providers would consume more time and resources than the benefits they would bring, it may be more practical to keep data labeling in-house.

Advantages of Outsourcing Data Labeling Work

For most projects, outsourcing data labeling offers several compelling advantages. Outsourcing data labeling to professional teams allows you to focus on building your AI/ML models by taking care of finding and preparing datasets tailored to your needs. The key advantages of outsourcing include:

  • Enabling your team to concentrate on the core development aspects of training models

  • Freeing up time by eliminating tedious annotation work

  • Providing access to high-quality training data that meets all necessary criteria

  • Delivering customized training data tailored to your project requirements

  • Reducing overhead costs, allowing for the allocation of resources to more critical endeavors

By evaluating your project's specific needs and the capabilities of your in-house team, you can determine the most suitable data labeling strategy—whether that involves keeping it in-house for select situations or reaping the benefits of outsourcing.

6 Key Questions to Ask Before Outsourcing Data Annotation Work

What kind of data is required?

There are four primary data types needed to effectively train machine learning models: Image / Video, LiDAR / Radar data, Audio / Speech, and Text. The type of data required depends on various factors, such as the use case, model complexity, training method, and diversity of input data.

Which annotation techniques will be employed?

Different data labeling tools and companies may have limitations or specialize in specific annotation methods, such as bounding boxes, semantic segmentation, or entity recognition. Understanding these capabilities is crucial when choosing a service provider.

What is the timeframe and budget for the data labeling project?

Consider the project timeline and whether the data labeling service can meet your deadlines. Assess the provider's ability to scale up resources and maintain quality within the project's timeframe. Also, consider the cost of the data labeling service and whether it aligns with your budget. Evaluate if the service is worth the investment and if it meets your project's expectations.

How will the project's efficiency be assessed?

Determine the Key Performance Indicators (KPIs) for your annotation project and ensure they are clearly communicated to your service provider. Clear requirements for data annotation are also essential for delivering efficient results and supporting the success of your computer vision pipeline.

Does the annotation team have a dedicated platform?

The right tool can lower costs and increase efficiency. If the annotation team lacks a data labeling platform, you may need to invest in a scalable platform, which could increase your overall expenses.

What are the data security and privacy measures in place?

Ensure the data labeling service provider follows adequate security and privacy protocols to protect your sensitive data. This is particularly important when working with personally identifiable information (PII) or other confidential data.

BasicAI: A Trusted Data Annotation Partner

When you have a team of dedicated, trained, and experienced data labeling experts working exclusively on your project, high-quality work delivered on time is guaranteed. Over the past 7+ years, BasicAI teams have successfully labeled 300,000+ datasets of all types for thousands of global clients. With our experience in diverse data sets and data labeling capabilities, we confidently deliver enhanced data labeling for ML and AI projects.

✅ Proprietary Annotation Platform

Developed by the BasicAI team, BasicAI Cloud is an AI-powered multimodal training data platform born for large collaborative data labeling projects. Specially optimized for computer vision data, BasicAI Cloud features:

Annotation Tools Available on BasicAI Cloud

Partnering with BasicAI Cloud means you don't need to worry about the data labeling tool—we have the best-performing one.

✅ Effective Processes with Efficient Output

With our experience in providing data labeling software and services for thousands of global customers, we've established a practical and mature process that ensures timely delivery and meets your requirements. BasicAI provides a dedicated project manager for every data labeling project, who oversees progress, quality, and satisfaction. As the client, you have full control of your project, and our annotation teamwork management module allows you to monitor progress and performance in real time.

✅ Cost Saving

At BasicAI, we understand the importance of cost efficiency in your AI and ML projects. Our AI-powered workflow not only elevates the quality of data labeling but also significantly boosts productivity, enabling us to offer competitive pricing in the market. By optimizing the annotation process with our innovations, we reduce manual labor hours and related expenses, passing those savings on to you.

We assess each project individually and provide a customized offering based on your specific needs. Our flexible pricing model accommodates projects of varying complexity and scale, ensuring you get the most out of your investment. Additionally, our platform's advanced features, such as auto-annotation and object tracking, further streamline the data labeling process, resulting in even greater cost savings.

✅ Quality Assurance

High-quality training data is crucial for successful ML models, and BasicAI embraces a tailored approach to data labeling quality assurance (QA). Our platform's powerful QA modules are designed to ensure both the accuracy of labeled data and compliance with your project's specific requirements.

Setting Up Quality Assurance Rules on BasicAI Cloud

We set up comprehensive QA rules aligned with your project's goals and employ a combination of automated checks and human expertise. The platform continuously monitors the annotation process, flagging deviations from established guidelines, while our experienced QA specialists perform manual checks to capture subtle nuances.

By merging AI-driven quality checks with human precision, BasicAI delivers exceptional data that serves as a solid foundation for your AI and ML models.

✅ Data Security

At BasicAI, we prioritize data security and client trust. We fully comply with the General Data Protection Regulation (GDPR), adhere to data protection best practices, and have achieved ISO 27001 and ISO 9001 qualifications. We implement necessary measures to minimize the risk of data breaches and provide the highest level of protection for our client's data.

✅ A Scalable and Flexible Solution

Data labeling is labor-intensive, and AI projects typically require thousands of accurately labeled datasets. With BasicAI, you'll enjoy constant support from 160+ selected global annotation teams with expertise, experience, resources, and skills to effortlessly scale your project. Our data labeling services have proven results in providing high-quality training data for industries like autonomous driving, smart agriculture, and smart cities.

Fuel Your AI with BasicAI. Talk to BasicAI Experts About Your Data Labeling Requirements Today

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