The success or failure of your artificial intelligence (AI) project depends on your choice of data annotation tools that you use to improve the quality of the data you use for training and enabling machine learning models. Unfortunately, choosing the right annotation tool is difficult, as the ecosystem changes quickly. More providers are coming up with new data annotation tools for a more diverse lineup of use-cases, as you can see at https://dataloop.ai/solutions/data-annotation/. The changes create improvements on the existing tools, aside from developing new ones.
With the availability of more annotation tools, the challenge now is to think strategically and consider your current and future needs as the competition in the marketplace increases, too.
When choosing the data annotation tools you need, you should consider several features because your team will have varying needs based on the type of data they are handling. Here are the essential features to look for in data annotation tools
An annotation project begins and ends with the correct management of datasets. It is the core of the workflow, and as an annotator, you must ensure that the tool you will use can import and support the volume of file types and data you have to label. The tool should also be capable of searching, sorting, cloning, filtering, and merging the datasets.
Consider the output requirements of your team, as annotation tools save the output differently. The tools should also integrate with the target storage spaces.
The primary feature of data annotation tools is applying labels to your data. Many tools are optimized for particular types of labeling. As there are tools with more capabilities, your choice of tools will depend on current and anticipated work and needs. You can either go with a general platform or choose a specialized tool. You should check if the data annotation tools include building and managing guidelines, like label maps, attributes, classes, and specific annotation types.
Many data annotation tools today use AI to automate several annotation processes. They call it auto-labeling, which helps human labelers enhance their annotation, like automatically converting a bounding box with four points into a polygon.
Data quality control
As the project involves teaching a machine model, you need high-quality data to ensure that the machine will learn correctly. Choose the tools that can help you manage the verification process and quality control – features embedded in the annotation process. This could include real-time feedback, issue tracking, and labeling consensus. The tools could also have a quality dashboard, making it easier for project managers to view and track issues regarding data annotation quality.
Whatever type of data you are working on should be secure. The annotation tool should have some built-in security feature to limit the annotator’s access to data not assigned to them. Likewise, the tool should prevent downloads of any data. Finally, discuss regulatory compliance issues with your data annotation tool partner to ensure that you will remain compliant.
Before purchasing your data annotation tools, you must first evaluate them and their features. Specifically, the tools should offer flexibility that will fit the needs of your annotation team.