top of page
  • Hang Dang

Factors to consider while choosing data annotation service providers

If you've ever built a machine learning algorithm, you'll know that collecting labeled datasets is a tremendous undertaking. To accomplish this step, the first thing you and your team need to focus on is how to efficiently and accurately collect and annotate data to effectively train your AI model.


What are the ways to make your data annotation done?

To complete data annotation work, most companies (especially SMEs) have 2 main paths for choosing today.

  • The first one is purchasing a full-packaged data annotation service from an outsourcing company so you do not have to do anything except for placing your requirements, money and receiving the results.

  • The second one is to use a data annotation platform so you will have a tool and will build up your own business in-house annotator teams or hire crowd-sourced workforce and project managers.

If you are an SMEs with limited resources in time, money, and human resources, outsourcing data annotation services is a proven way for teams to boost productivity, decrease development time and stay ahead of the competition. Individuals, researchers, companies and governments are also increasingly turning to data annotation companies as a viable solution to obtain both crowdsourced annotators and off-the-shelf annotation tools.

As the number of AI training data service providers grows, how do you decide which to trust? In this blog post, we will discuss some important factors to consider while choosing a data annotation service provider.


Key factors for your consideration



Accuracy

The most important factor to consider while choosing a data annotation service provider is the quality of the annotations. The annotations should be accurate, consistent, and of high quality. A good data annotation service provider should have a team of trained and experienced annotators who can handle complex datasets and provide high-quality annotations.


Beside the human resource, make sure the provider has a robust quality control process in place that checks the accuracy and consistency of the annotations at different stages of the project. Normally, a good provider should have at least 2 rounds of review and quality assurance to check if all the annotated data meets your requirements.


Turnaround time

If you require quick turnaround times, it's recommended that you opt for a provider that can deliver fast results. It's advisable to choose a data annotation service that utilizes advanced tools such as pre-annotation, semi-automatic, or auto-annotation labeling pipelines, which can speed up the data annotation process compared to traditional manual methods.

It's also crucial to investigate their previous projects and the time it took them to complete them, as this can give you an idea of their capabilities and the types of tasks they can handle.


Cost-effective

The cost of data annotation can be influenced by various factors, such as the time required to annotate your data, the urgency of the task, and any additional services like custom labeling that may come at an extra cost.


When choosing a data annotation provider, it's crucial to examine their pricing structure. Do they offer different tiers based on the amount of data annotations? Do they charge by the hour, or do they provide discounts for bulk orders? These queries will aid you in finding the most suitable pricing plan that meets your requirements.


File formats accepted

It's crucial to ensure that your chosen data annotation provider can support the file formats you have, particularly if you're dealing with a vast amount of data in various formats. If you're uncertain about which provider to select, you should inquire about the file formats they accept.


In cases where you have multiple data types in your system and not just one format, finding a provider that can handle all of them might be more challenging than it's worth. Hence, it's advisable to confirm if the provider can import both images and videos since this feature can be useful when annotations involve videos instead of photos.


Scalability

The ability to scale up or down the annotation services according to the changing needs of the project is really crucial. A good data annotation service provider should have the infrastructure and resources to handle large volumes of data and be able to scale their services as the project grows.


Before making a decision, you can gather more information about data annotation providers by asking a few questions. For example, how many annotators can the provider hire at once? Can they easily scale up or down if the volume of work increases or decreases? Are they capable of meeting deadlines with ease? Can they adjust their workflow promptly to save costs, or would it be difficult to modify their processes based on the amount of work they receive each month? These are essential queries to ask before selecting a data annotation service provider.


Security and Privacy

When it comes to data annotation, ensuring data privacy and security is of utmost importance. If you're using a cloud storage service, it's crucial to have safeguards against hackers and malicious parties. You should verify whether the provider has appropriate encryption protocols in place to safeguard against phishing scams or ransomware attacks. It's also worth asking if they keep their security measures up to date with current trends in cyberattacks.


A good data annotation service provider should have robust security protocols to prevent unauthorized access, theft, or misuse of data.


How to Find a Data Annotation Service provider?

When searching for an outsourced data annotation and dataset labeling provider, it's important to approach it with the same principles used when outsourcing any critical service.

To begin with, start by leveraging your network and ask for recommendations from people you trust. You can also check with providers your organization has worked with in the past. It's also essential to compare and contrast different providers by reading reviews, case studies, and assessing their experience and sector-specific expertise. While price is a crucial factor, it's important not to choose the cheapest provider, as this could result in disappointment and wasted time if they are unable to deliver the expected quality.


One approach to identify the most reliable provider is to test several providers simultaneously using a proof of concept (POC) dataset. This method allows you to benchmark and assess the quality and accuracy of the annotations produced by each provider. The results of the POC can then be used by in-house data annotation and machine learning teams to determine the most dependable provider to work with for long-term and high-volume dataset annotation projects.


Some trusted data annotation service providers



Pixta AI is a company specialized in providing full-packaged services in data sourcing and data annotation with unparalleled experience in image, video, and LiDAR annotation from Vietnam.


With over 8 years of experience in computer vision and data annotation, Pixta AI has a large and experienced team of annotators and a 80M+ full-compliance visual data from the company's library PixtaStock - the largest library of royalty-free photographs, illustrations, and footage in Asia. In addition, Pixta AI applies the most cutting-edge tools with pre-annotation and semi-automatic labeling pipelines, which allows them to annotate data up to 3 to 4 times faster than traditional manual methods, optimizing both cost and time for all projects.


In 2022, Pixta AI's Data Annotation service grew incredibly with 15+ customers including Honda, Panasonic, Casio, 7andi, SoftBank, NTT Docomo, Mitsubishi Electric, etc., in a single year. The company provides diverse services in various industries with all types of labeling: bounding box, image segmentation, Lidar, etc.


If you are in need of high-level data annotation projects that require high difficulty and accuracy, full-compliance data set with a tight budget, Pixta AI is an excellent choice for your consideration.


Price: From $0.01/annotated with Free pilot project and quality commitment


Dataclap is a company based in India specializing in providing full-package data annotation services. They are a quality driven organization with the key values of transparency, ownership and commitment to customers. They provide data collection and annotation services for companies in domains ranging from autonomous vehicles to sports analytics.


As DataClap is the preferred training data partner for startups in the AI, ML, NLP, and Computer Vision space, if you are a startup company or an institution looking for small projects, DataClap will be a good choice for you.


Price: From $0.025/annotated with Free pilot project but no quality commitment


LabelYourData is a 10-year experience to help AI companies grow faster by providing fast and secure data annotation services for their projects. Originally founded in Kyiv, Ukraine, the heart of Eastern Europe’s booming tech scene, LabelYourData turned global with offices in many countries around the world while keeping focused on providing professional data annotation services for any industry including Autonomous Vehicles, Retail, Robotics…


LabelYourData can provide you with Secure Data annotation service with high quality annotations, flexible business model, strict quality assurance process and customized annotation solutions,...


Price: Free pilot project and quality commitment


Bottom line

In conclusion, choosing the right data annotation service provider can be a daunting task. However, by considering the mentioned essential factors, you can find a provider that can deliver high-quality annotations, customized solutions, and exceptional customer support, all within your budget and timeline.


Experience Pixta AI in action. Reduce manual image annotation tasks, generating massive savings and efficiencies. Try it for free today.



49 views0 comments

Comments


bottom of page