Finding Data Annotation Specialists — A Key Challenge for ML Projects

A scientific approach and thoughtful evaluation streamline the process to shortlist a good data annotator. With an evaluation framework at your disposal, you can easily mitigate the shortlisting challenges and accelerate the process, making faster AI execution possible.

Ridden with challenges, the process to shortlist a good data annotator can be streamlined by following a scientific approach and thoughtful evaluation. With the rise of AI for business, data annotation is getting equal traction. Up to 2027, a CAGR growth of 32.5% is expected in the data annotation space. So AI and data annotation growth are going parallel, as they should ideally be. But with their core focus on analytical activities in AI implementation, technical stakeholders are always in favor of outsourcing data annotation.

However, many issues stand before companies while they search for good data annotators. These usually revolve around labeling efficiency caused by manual approaches. Additionally, data annotators use crowdsourcing that does not produce quality datasets. So, while the rise of AI has resulted in the rise of data annotators, finding a performing data annotator requires dedicated efforts.

Keeping this in mind, we will discuss in this blog how you can simplify the search for a good data annotator. Beginning with typical challenges in this process, we will proceed to the selection approach.

What are the 5 key challenges in finding data annotation specialists?

Some of the common challenges that act as hurdles for companies who are searching for quality data annotation specialists:

1. No access to practical results

It is easier to get access to case studies that boast of significant improvements. However, these theoretical claims of data annotation providers don’t provide actual insight into practical improvements that were derived in the executed ML projects.

2. Lack of image editing skills

Data annotators do not necessarily be image editing specialists, while they might have expertise in other areas like text. To shortlist a data annotator who can effortlessly handle image annotation, you may need to widen your search.

3. Difficult to discover experts

How do you know that the one you shortlisted has a fair amount of experience in handling a variety of techniques? So you need to base your screening based on multiple parameters.

4. Lack of details about projects

“Number of projects” executed by prospective data annotation partner is never enough. However, it is never easy to understand the magnitude of projects (client profile, data volume, resource allocation, scalability efficiency, etc.)

5. Difficult to find annotators cum AI, specialists

To remain ahead in the competition, you will always try to get data annotators who thoroughly understand the functioning of machine learning. With each data annotator claiming to be like that, practically it is never easy to find data annotators who are ML experts too.

How to choose the right data annotation specialist

Here are some crucial factors, which if you consider seriously, will allow you to select the right data annotation specialist.

Develop a statement of work (SOW)

Beginning with a statement of work is an ideal step to select the most promising data, annotation partner. It will let you define the scope of your data annotation project and cover all key performance indicators. The statement of work will keep acting as a baseline to help you determine if the vendor has adhered to the SOW.

Ideally, a statement of work for data annotation projects should clearly define success criteria against important machine learning metrics. Such metrics will usually include model accuracy and precision, mean absolute error, logarithmic loss, etc.

Assess the ML project management experience

Just managing projects and managing projects following standard project management guidelines are two different things. Involving multiple complexities, machine learning projects are difficult to manage and so require a scientific management approach. You must, therefore, develop an evaluation framework to assess project management skills.

Interact with data annotator project manager before beginning the project. Once you are satisfied with the theoretical evaluation, you can request a pilot project. A step-by-step evaluation will give you a fair idea of the project management skills of the data annotator.

Evaluate capability to handle diverse data annotations

Data annotation majorly revolves around four types viz. image annotation, text annotation, video annotation, and audio annotation. Despite the claims of the annotators, it may or may not be possible for them to demonstrate equal efficiency in each of these areas. So, you must drive a critical evaluation.

Begin with the data type of your interest. Request a proof-of-concept (POC). If your focus area is image annotation, use evaluation to ensure that the annotator, indeed, is an expert in image annotation. Imagine you look for an image annotation specialist, but the annotator is an audio annotation specialist. Will it help? So, evaluate.

Familiarize yourself with the workflow

Understand and keep in mind that data annotation is an intrinsic part of your machine learning process. It is a part of that 80% of efforts that go into building quality data for the well-functioning of machine learning modeling. And for annotation to be in tandem with your analytical process, you must be aware of the roadmap.

The roadmap gives an initial idea as to how data annotators will drive the process. By detailing the course of actions, it allows you to determine if the implementation will work right for you.

Evaluate integration capabilities

Data annotation works in an external environment which needs to be integrated with the client’s environment for smooth dataset creation. Substandard integration can adversely impact the efficiency of data annotation and so evaluating integration capability is a criterion to selecting a data annotator.

Evaluate if data annotators can easily import data from your internal environment. Understand what kind of integration mechanisms they are using — self-hosted support systems, cloud-based systems, or partner-hosted systems. You can tap in their applications’ details to assess the data volume handling facilitated by the integration.

Never compromise data security

Being keen on producing excellent datasets for your machine learning or computer vision models doesn’t mean that you should compromise data security. What does this mean? Remember, you are handing all your data to an external agency and so you must ascertain their security framework.

Data annotators must strictly follow local and global data protection guidelines and protocols like GDPR. By evaluating them, understand if their security system is secure enough to guard your data. Make data security an important parameter to finalize the deal with the prospective annotator.

Technology usage

Although data annotation still relies on a manual approach, you may look to collaborate with a techno-savvy data annotator. You may desire to increase the pace of your machine learning implementation and technology data annotation might interest you.

For this, check with the partner the amount of technology they use and if using AI for annotation forms a part of their SOP. Your extreme data volumes may make you look for big data plus AI-based data annotation support. Create a checklist to confirm from the prospect.

Conclusion

Annotating data is a special task and the sole determiner of your machine learning model success. A series of elements and concepts define this success. Usually, these are data annotation tools, labeling platforms, data annotation techniques like semantic annotation, 3D points, polygons, and polylines, etc. So, you must outsource, but only after careful evaluation.