Labelling and annotating data is usually done manually using a specially designed software called a data annotation tool. Data annotation tools can be used to add labels to various data types....
Data serves as the foundation for any artificial intelligence or machine learning project. An AI/ML model requires carefully annotated and labelled data, called training data, to learn how to recognise patterns and make decisions. The results an AI/ML model produces is heavily influenced by its training data, which is why high-quality training data forms the basis of an effective and successful model.
What are the different methods for data annotation?
Labelling and annotating data is usually done manually using a specially designed software called a data annotation tool. Data annotation tools can be used to add labels to various data types such as images, text files, audio files, and more.
One of the most important things to consider when starting an AI development project is deciding how to obtain the initial set of training data.
In general, there are four main ways of converting raw data into training data:
- Using open source tools with internal annotators
- Using paid platforms with internal annotators
- Paying a vendor to annotate data with a specified platform
- Paying a vendor to annotate using their own platform
It can be difficult to decide which method is most effective for your needs, so let's go through their different aspects.
Using open source tools with internal annotators
This first method probably seems like the simplest and cheapest solution, and it can be, but there are a few issues with this method that can easily be overlooked.
Issues with using open source tools typically arise when trying to scale a project up, as many of these tools are more suited for smaller projects and teams. It is analogous to writing a long article collaboratively before the advent of tools such as Google Docs and Office 365 - errors such as missing data, conflicting annotations and low annotation quality will become increasingly commonplace. Another thing to consider is that, while open source tools are free, a project still needs team members with technical knowledge on how to deploy and develop workflows around the tool.
In practice, an open source tool is best used for individual projects or prototyping an idea for an AI/ML model rather than large scale operations for business.
Using paid platforms with internal annotators
In the last few years a number of data annotation platforms have been created and made available for purchase as more companies look to adapting AI/ML models. Such platforms typically come with project management features, making it easy to scale up your data annotation work.
Using a data annotation platform allows you to once again avoid the obstacles that usually come with modifying open source software or developing your own annotation platform, enabling you to direct resources elsewhere in the project while accelerating its timeline. An advantage paid platforms have over open source tools is that the cost of supporting and upgrading the tool is borne externally by the tool provider instead of internally. Choosing to purchase a data annotation tool is a quick solution to getting your data labelled with a large team, however, it also comes at the cost of less customisability than you would get with a purpose-built annotation platform.
Another aspect to consider for both this and the previous method is the use of internal annotators. While it is natural for a company to want to manage its own annotation staff, a training dataset usually needs hundreds of thousands of data points to be useful to the AI/ML model. For a company this would either mean having fewer employees spend excessive amounts of time labelling/annotating data or hiring more employees. In either case, the tedious and often manual process of annotating this data can lead to burnout and other human capital issues that may not be obvious at first glance.
Paid platforms can be a good option for less complex projects with fewer specific requirements, and when lacking team members with technical expertise.
Paying a vendor to annotate data with a specified tool
As previously mentioned, labelling and annotating data internally can pose a huge obstacle for an AI/ML project. Because of this it may be better for many projects to outsource this process.
A number of new companies and startups have formed offering professional data annotation services for AI/ML projects in recent years. Such services can be extremely helpful in reducing the workload of internal employees, allowing them to focus their efforts on other more important parts of development. Scaling a project is often much easier with a vendor as well given that the focus of their workforce is on annotating the data. Effectively, this method ‘buys’ the labour from another company to work with a tool that you specify.
One thing to consider when hiring an external team to use your tool of choice is that it may take some time before proper workflows and quality standards are established as the team gets accustomed to the platform. In addition to this, supporting the software and ensuring it functions properly would still fall on the provider of the platform, and the workforce that you’ve hired may not necessarily synergise well with the tool you’ve chosen.
Linking up with a vendor to have your data annotated is most effective for projects with a larger scope and when looking to reduce internal workload. However, there are also vendors which offer annotation services on their own custom-built platforms, which brings us to our next method.
Paying a vendor to annotate using their own platform
Many vendors have their preferred annotation tools, or indeed have built their own tools which are suitable for their own workflows.. Allowing a vendor to use their choice of platform enables them to make changes where necessary to fit your specific needs.
Delegating the choice of annotation platform to the vendor allows them to be more flexible and operate with more efficient workflows than other options. This method is the most comprehensive of the four in terms of annotation services because the vendor handles all aspects of the annotation processes listed in previous methods. The learning curve is gentler than mandating a specific tool, and it removes the need for excessive intervention on the client’s part.
Another advantage of this method is that it can lead to closer partnerships between the vendor and the client. A client is able to specify their project needs and the vendor will usually determine the best course of action for their requirements while keeping in mind accuracy, speed and costs.
This method is popular among larger companies looking to have their annotation needs handled professionally and with minimal need for intervention.
Choosing for your needs
Data labelling and annotation is a growing industry. As more companies continue to integrate AI/ML models into business operations the need for quick, accurate, and cost-effective data annotation services will rise. Tictag provides a comprehensive data annotation service through a crowdsourced approach using our mobile app, while benefiting the community. Find out more about how you can join us as a tagger to earn rewards for labelling data, or contact us to find out how to get your data labelled quickly and accurately.