Artificial Intelligence (AI) and Machine Learning (ML) have assumed a leading role in the digital revolution as a result of recent advances in science and technology. However, creating and developing AI or ML-based applications, which involves labeling innumerable number of datasets to train the model, is not that easy, especially when it comes to data labeling for SMEs. Whether your machine is an autonomous vehicle, facial recognition, medical imaging, surveillance, or any other AI application, it still needs to go through a certain process of data annotation to produce the required dataset for AI or ML training.
Taking the scalability of AI projects into consideration, the requirement for the amount of annotated data is usually beyond the imagination of the AI SMEs let alone the concerns for human resources and the limited budget. Therefore, outsourcing data annotation projects is always among the best solutions and will help your SMEs not only successfully get the required data but also gain numerous benefits.
In this article, as a company providing data labeling services, TagOn will provide data labeling for SMEs in need with useful information for having a clear understanding of how to effectively outsource their data labeling projects.
What do data labeling for SMEs and outsource mean?
To start with, it is important to mention what the term data annotation, SME and outsource exactly mean. Basically, data annotation is the main core of computer vision. The way the computer processes visual data often falls short of the way human brains do since in order to make decisions, the computer has to be explained what it is interpreting and provided the context. And data annotation shoulder this responsibility and act as a connection in this workflow. To be more specific, in machine learning, data annotation is the process of identifying raw data including images, audio, text, video, etc., and then label to a certain meaning and context. These annotated data are then used to train the artificial intelligence and help the machine learning model learn. In other words, data labeling is the process of producing training data for an AI- and machine-learning-based visual perception model.
You can also learn more about what is data labeling definition and its role in here https://tagon.ai/what-is-data-annotation-and-why-does-it-matter/
SME, on the other hand, is a small-to-middle- size enterprise with revenues, assets, or workforce below a specific threshold. Data labeling for SMEs therefore is much different from data labeling for big corporates with extensive financial capacity. Referring to the term outsourcing, it is a business practice that involves hiring out services or job duties from a third party on a contract or ongoing basis.
When is the time to outsource your data labeling projects?
Outsourcing data labeling for SMEs is a big decision, hence, it is necessary to determine whether your company is in a suitable time to outsource or not. However, the question is how would you know when your company is ready to outsource? The below information will help you to answer this question with 5 prominent signs.
When your company is growing too fast
Startups, new businesses, or established businesses that are going through a period of growth mode often face numerous challenges as well as problems when the product has been constantly improved or launched, and the number of your customers has been exponentially increasing. At this time, what your company needs is to focus on controlling and managing the companies to make sure there would cause no risk while growing fast.
Therefore, outsourcing the third party to accomplish things that are not your core products will provide you with a safe path for outstanding development. And clearly, it is exactly when you need to search for “how to outsourcing data labeling for SMEs”
When you need to focus more on your business
Another consideration of outsourcing data labeling for SMEs is that you need to spend more time and energy on scaling up your business. On the first hand, when your company is scaling up, it is necessary to mention to the human resources, which will have a specific impact on your company workflow and efficiency. However, the process of hiring employees will cost you not only a large amount of time and money, but it will also stall the operations in other departments of the business, especially in the case of data labeling projects recruitment, which often requires a huge number of annotators to deal with hundreds or even thousands of dataset, let alone the high quality and experienced ones. Therefore, if your SMEs are in this case, it may be a sign for them to outsource data labeling projects, which can easily provide the required annotators at any time, in any case.
On the other hand, when scaling up your business, AI SMEs will need to focus on the business-critical rather than spend time recruiting annotators or forcing your employees to annotate data, which is not their major or core mission. If it is your company’s need at the moment, it is the right time for your SMEs to find a third party to outsource their data labeling projects.
When your company is burning out
Another sign showing your SMEs are needing to outsource is that your companies are in a period of burning out. Specifically, all of your employees are exhausted due to the high work intensity, and it’s not always a good idea to push yourself and your team to work extra overtime for a longer amount of time. Moreover, it will be even worse when these exhausted employees can not meet the requirements or reach the KPI of annotated data, leading to numerous dangerous delays and missing deadlines. If these situations appear in your companies, they are absolute indicators that a company is prepared to outsource.
When you need to lower the cost
When you’re ready to save costs and improve the effectiveness of your AI back-office operations, it will be the right time for outsourcing data annotation projects. As data labeling for SMEs takes lots of steps, if your company want to forward the fee paid for recruiting, building, and training your in-house annotators or the money spent on paying for overtime to the investment on the core business, you will know that your company is necessary to outsource the third party for labeling data.
When your companies have not invested in technology or infrastructure
With AI startups or middle-size companies, their early-stage development needs a number of tools to deal with data annotation. However, if your companies have not invested any in building suitable technology or infrastructure, it will be a sign for you to outsource data labeling projects rather than spending lots of time and money on building and then waiting for a finished system with high-quality tools and workforce to exist.
Why SMEs need to outsource their data labeling projects?
When you find any signs your SMEs are currently facing, it may come to the question “Why should my company outsource data annotation projects?” With this question, we will list out 3 main reasons that outsources will bring your SMEs benefits.
It is clear that one of the biggest problems that data labeling for SMEs often face is the problems related to the quality of their annotated output. This is because new-built companies or even middle-size companies have few effective materials including both tools and high-quality annotators for labeling data. However, these problems can be easily solved if your company outsources since the third parties not only have enough equipment but also can provide expert data annotators who have been trained to meet the right domain expertise requirements. Therefore, outsourcing will ensure your company will have high-quality annotated data with the help of both technologies and experts.
When your companies are developing an AI model, you will never know exactly how much data it will need to learn. Therefore, flexibility is among the top priorities. With a data labeling outsourcing agency, your company can ensure flexible scaling up. Specifically, the hinder for data labeling for SMEs to scale up may lie in the annotator forces. With such a small number of annotators, your company certainly cannot meet the demands once the AI model requires more annotated data than what your force can provide. The result is that there will be delays and low performance, which will directly negatively affect the learning process of your AI model. If your company hires a data labeling company, this challenge will become much easier to overcome since outsourcing can offer sizable, on-demand, and qualified individuals to effectively perform those tasks. Moreover, this data labeling company can both rapidly recruit data annotators and can easily ramp up and scale up and down the workforce of annotation professionals as project demands change. This means that your company can increase or decrease the number of needed annotators based on the scale of different data labeling projects. Therefore, this will increase both quantity and quality as well as the speed for scaling up the projects.
Mitigating internal bias
In essence, protocols, processes, workflows, techniques, philosophies, work culture, etc. frequently bind a corporation. As a result, it is possible for any single employee or team member to hold beliefs that are quite similar. Furthermore, there is undoubtedly a danger of bias emerging when such unanimity of forces is used to annotate data.
Given that training datasets are one of the first places where bias may appear, it is advantageous to delegate the task of minimizing bias to the third party, who then provides accurate and diverse data.
It is obvious that SMEs are those which have a limited budget. Therefore, one of the top goals of data labeling for SMEs is to achieve price optimization by having wise investments. However, AI companies, which have to deal with annotating data serving for AI or ML training, require a large amount of money to maintain a team of in-house annotators. However, it is noted that there will be the time that is unnecessary to have so many annotators, therefore, the fee for maintaining them is not optimized, not to mention the low quality of their products due to the lack of experience, time, or even energy. In this case, outsourcing will solve the problem, and provide you with the best-optimized solution since it can not only ensure the flexible quantity of annotators but also the quality of labeled data. Moreover, outsourcing will provide you with a wide range of vendors with different prices, enabling your SMEs to choose the best service provider with the most reasonable fee. In other words, outsourcing your data annotation projects will be a wise investment for SMEs.
What steps to take for successful outsourcing data labeling for SMEs?
Although outsourcing the third party to label your data set can bring your SMEs lots of benefits, in order to successfully outsource and get satisfied output, you still need to go further into how to outsource, and what steps you need to take for outsourcing.
Lay down the detailed outsourcing goals
At the very first step, what you need to do is lay down what your goals are for specific data annotation projects. You need to know why you need to outsource your projects, what you can expect from your partner, and other important factors including the time to finish your projects, the quality and quantity of received data, and so on. With these detailed goals, your company can have a well-prepared and clear orientation to follow, thus making the outsourcing process much more efficient.
Strike a Partnership with the Right Outsourcing Company
Partners play an important role for outsourcing data labeling for SMEs in deciding whether your projects will be successful or not. Therefore, you should bear in mind that you must avoid being duped by flashy websites, low prices, or slick salespeople making outlandish promises. Instead, you should ask around, research the business’s history, testimonials, infrastructure, price, and management. Moreover, you should also remember that a provider who recognizes and meets your company’s needs is what would be the greatest for you. Therefore, prepare detailed criteria and a clear job description for your data labeling company to have the finest partners
Mitigate outsourcing risk
After having imagined your ideal partner for your SMEs, it is high time to have a backup plan for all possible risks when outsourcing your data labeling projects. Firstly, during your partnership, there may be some problems related to trust issues pop up. Therefore, make sure that you can build a strong and committed partnership based on a strict legal contract that has received agreement from both parties before assigning them any further tasks. Especially, you must have a clear statement on Confidentiality and Intellectual Property Rights, or else it will put you at risk once the data or anything related to your AI model is leaked publicly. Moreover, you should also prepare a budget to spend on unexpected expenses since during the process of labeling data, the hidden cost may incur and cause you lots of difficulties. By being well-prepared in terms of money, you can relieve a lot of financial burdens later. The last thing to mention is quality issues. It is clear that when you invest in outsourcing projects, you will never expect to review low-quality annotated products. As a result, mitigate this risk by testing the potential vendors thoroughly before choosing any service providers.
Delegate and restructure
Once the contract has been signed, it’s time to assign tasks. Your new employees will begin being hired by and trained by your outsourcing firm of choice. Therefore, what you need to do is actively be involved in this process to make sure that every selected annotator will meet your requirements. Furthermore, if your company used to have employees in charge of data labeling tasks, you should assign them another task, which is more suitable to their major and ability. By doing this, your workforce can be restructured to shoulder their own responsibilities.
Evaluate the performance
During the project, your company should control and have timely feedback if necessary throughout the adjustment period since the performance will have a great impact on the annotated data that you will receive. Specifically, you must continuously assess how well your offshore activities are performing, identify any gaps, and develop solutions for them. Moreover, employee education and training should be focused and increasingly improved. Additionally, you need to modernize your current systems and procedures while keeping an open mind to new ideas. After the evaluation, you can consider whether you can expand the contract with your chosen vendor or you will choose another one for better performance. Therefore, make sure that you take part in any stage of this process to have the best measurement.
Successfully outsourcing data labeling for SMEs has never been an easy task since there are a lot of things that your company need to take into consideration. However, with this article, we hope that you can have a clear understanding of how to find the best data annotation service providers. And if you are wondering which provider can bring you the best solution for data labeling, you can consider TagOn, which can Accelerate your AI developments with TagOn advanced data labeling platform for all types of projects, while can optimize all options for SMEs.
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