Without a Data Annotation Tool, the AI Data Processing is impossible. Some might choose the open source tool, some might come for the vendor source tool or even develop their own tool. With either of these, it is essential for businesses to consider top 5 annotation tool features to find the most suitable one for their products:

• Dataset management
• Annotation Methods
• Data Quality Control
• Workforce Management
• Security

What’s a data annotation tool?

A data annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to annotate production-grade training data for machine learning. Cloud-based data annotation tool (SaaS) is a tool built on top of a cloud platform. With objects being stored in the cloud, training data is reliably secured. A team can easily simultaneously annotate multiple datasets in real-time without any glitches. On-Premise data annotation tool is a tool used within the premise of a business. On-Premise tools are favoured due to the data security and the quick response whenever issues occur. This kind of tool often requires licenses to use and higher costs for maintenance and management.

 Common AI data processed include image annotation, video annotation, voice annotation, and text data annotation. You can either buy/lease a data annotation tool or build it yourself. It depends on how you want to manage your datasets and your requirements regarding security and frequency of customization. No matter what approach you choose, you always have to go through the phase of analyzing your projects.

These are some requirements you need to clarify before going into any AI Data Processing project:

• You want to begin a machine learning project. You already have the data you want to clean and annotate to train, test, and validate your model.
• You have to work with a new data type and need to understand the best tools available for annotating that data.
• In the production stage, it is a must to verify models using human-in-the-loop.

After clarifying these three requirements for your annotation process, businesses can easily identify which annotation tool is the best for their firm by going through five features of any annotation tool.

Also read: 

[Infographic] A Comprehensive List of Data Annotation Tools

Fundamental guide to ensure Data Labeling quality

5 Important Data Annotation Tool Features

Annotation Tools play a vital role in the success of the whole annotation process. They not only help boost up the speed and output quality, but also assist businesses in management and security. By the way, the features below may also help you to understand the whole process if you are interested in a job as a data specialist.

 

1. Dataset management

Annotation begins and ends with a comprehensive way of managing the dataset you plan to annotate, which is a critical part of your workflow.

Therefore, you need to ensure that the tool you are considering will actually import and support the high volume of data and file types you need to label. This includes searching, filtering, sorting, cloning, and merging of datasets.

Different tools can save the output of annotations in different ways, so you’ll need to make sure the tool will meet your team’s output requirements.

Your annotated data must be stored somewhere, so it is necessary for one to confirm support-file storage targets.

Another thing to consider when forming dataset management is the share and connect ability of the tool. Annotation specifically and AI data processing generally are sometimes done with offshore agencies, therefore the need for quick access and connectivity to the datasets.

 

2. Annotation methods

This is considered the core feature of data annotation tools – the methods and capabilities to apply labels to your data.

Depending on your current and anticipated future needs, you may wish to focus on specialists or go with a more general platform.

The common types of annotation capabilities provided by data annotation tools include building and managing ontologies or guidelines, such as label maps, classes, attributes, and specific annotation types.

Moreover, an emerging feature in many data annotation tools is automation, or auto-labeling. Using AI, many tools will assist your annotators to improve their skills in labeling data or even automatically annotating your data without a human touch.

Some tools can learn from the actions taken by your human annotators to improve auto-labeling accuracy.

If you use pre-annotation to tag images, a team of data labellers can determine whether to resize or delete a bounding box. This can shave time off the process for a team that needs.

Still, there will always be exceptions, edge cases, and errors with automated annotations, so it is critical to include a human-in-the-loop approach for both quality control and exception handling.

 

3. Data quality control

The performance of your machine learning and AI models will only be as good as your data, whereas Data annotation tools can help manage the quality control (QC) and verification process. Ideally, the tool will have embedded QC within the annotation process itself.

For example, real-time feedback and initiating issue tracking during annotation are important. Additionally, these can support workflow processes such as labeling consensus. Many tools will provide a quality dashboard to help managers view and track quality issues, and assign QC tasks back out to the core annotation team or to a specialized QC team.

 

4. Workforce management

Every data annotation tool is meant to be used by a human workforce, even those tools that may lead with an AI-based automation feature. You still need humans to handle exceptions and quality assurance as noted before.

Hence, leading tools will offer workforce management capabilities such as task assignment and productivity analytics measuring time spent on each task or sub-task.

 

5. Security

Whether annotating sensitive protected personal information (PPI) or your own valuable intellectual property (IP), you want to make sure that your data remains secure.

Tools should limit an annotator’s viewing rights to data not assigned to her, and prevent data downloads. Depending on how the tool is deployed, via cloud or on-premise, a data annotation tool may offer secure file access (e.g., VPN).

Choosing an annotation tool is seemingly an easy task, perhaps because there are plenty of choices on the market.

However, no matter how many annotation tools there are to offer, your businesses are still under the risk of choosing the unsuitable one. To prevent this, you need to know the fundamentals of how to choose the right annotation tool, and necessary data annotation tool features to be put into consideration are security, HR management, data quality control, annotation methods, and dataset management.

 

Too busy to list these features out? Get consults from LQA to come up with data annotation services for your business. Contact us now for full support from experts.

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