Only a few data annotation tools were available commercially a few years back. So most users back then used whatever was available through open source. Others built their own tools to include artificial intelligence (AI) to develop an innovative product or solve a specific business problem.
More commercially-available data annotation tools appeared in the market around 2018. These tools carried full features to complete the workflow processes to label data. With access to new data annotation tools, project teams must decide if they should continue to build their tools or purchase professionally developed annotation tools, such as the ones at https://dataloop.ai/solutions/data-annotation/. But even then, they have to consider which of the tools available will make a better fit for their project and its requirements.
When should you build a data annotation tool?
Sometimes, even a full-featured data annotation tool will not meet all the requirements of the projects you handle. If this is the case, it might be better to build your own. Your annotation tool will provide you with the highest level of control from the start to the completion of the project, determining the type of data you will label and the outputs that your specific project requires.
Further, building your data annotation tool allows you to implement changes immediately using your in-house developers. As a result, setting your priorities and applying additional technical controls to improve data security becomes more manageable. Data annotation company provides data labeling outsourcing services for machine learning also provides low-cost, high-quality training data at scale.
However, there is a downside to building your own tool. There will be plenty of unknowns, and the scope of the requirements might shift quickly. The changes can cause loss of productive time. Moreover, you will be incurring additional overhead to change the infrastructure to develop and run the revised tool, including the development of new resources to maintain it.
When should you buy a data annotation tool?
Purchasing an annotation tool from a third party can accelerate the project’s timeline. You can start the project immediately, as the data annotation tool is already tested and enterprise-ready. The vendor understands the market, as they work with other customers. They can include the industry’s best practices in their software. Moreover, most commercial data labeling tools are configurable, so you can customize them to fit your needs.
Buying a commercial data annotation tool is less expensive. This is because you do not have to invest in its development and continuous direct support. Thus, you have more time to focus on the project. But there would be some compromise regarding workflow and specific use-cases because your option to customize and the level of control you can have over the commercial date labeling tool is lower.
In some use cases, the requirements may change, and the tools you have now may not support the project’s needs, which means you have to buy or build the integrations or get separate tools to complete the process.
Data annotation requires precision, and the answer to whether you should build or buy data annotation tools will largely depend on the type of project you handle. It is better to buy if you need plenty of data in different styles, manage various projects, and start the project immediately. On the other hand, you should consider purchasing a data annotation tool if you are concerned about reliability, continuity, usability, integration, scalability, and time to market.