Data annotation is an essential machine learning (ML) process that helps train and improve the accuracy of ML models. This involves identifying and labeling the data points or features used to train the model. This task can be tedious, but it is essential to ensure that the ML model performs accurately.
Several different data annotation tools are available, both commercial and open source. This article will explore the best data annotation tools available and guide you in choosing the right tool for your needs.
Several different data annotation tools are available, each with their unique characteristics. The following is a review of the most popular and effective data annotation tools, along with some tips on how to choose the best tool for your needs:
Business data annotation tools
Commercial data annotation tools are software packages that you purchase and install on your computer from an online store or CD / DVD. They do not require any special technical knowledge to be used, but they can be more expensive than open source or freeware alternatives.
Additionally, they often come with limited documentation and require customer support for installation issues, leading to increased costs. Some commercial annotation tools do not offer discounts to academic institutions.
These would be classified as “for-profit” sources of data annotation tools, while others do, making them a more affordable option for academic researchers.
These tools offer a wide range of functionality, including data cleansing, preprocessing, and feature engineering. They also allow you to build custom models and algorithms and deploy them in production. However, they can be expensive and difficult to learn, so they may not be suitable for everyone.
Benefits of commercial data annotation tools:
- Superior quality level: Commercial data annotation tools are generally more user-friendly and offer a higher level of quality than open source and freeware tools.
- Technical support: Commercial data annotation tools typically offer technical support, which can be helpful if you need help using the tool or troubleshooting errors.
- Rich in features: Business data annotation tools often include various features, such as data cleansing, data transformation, and machine learning, which can be useful for ML models.
Disadvantages of commercial data annotation tools:
- Cost: Commercial data annotation tools are generally more expensive than open source and freeware tools.
- Limited functionality: Business data annotation tools may not include all of the functionality that you need for ML models.
- Complexity: Commercial data annotation tools can be more complex to use than open source and free tools.
Open Source Data Annotation Tools
Open source data annotation tools are free downloadable software packages. They often come with extensive documentation and a range of helpful tutorials, making them easy to use.
Additionally, there are more open source annotation tools than commercial ones, which means if one doesn’t meet your needs, you can switch to another. The main downside is that the learning curve for these tools can be steeper and they may not offer all the functionality of commercial data annotation tools.
Benefits of open source data annotation tools:
- Free use: Open source data annotation tools are free to use and often include various features.
- Technical expertise required: Open source data annotation tools typically require more technical expertise than commercial or free tools. It can be a disadvantage if you don’t have the necessary skills.
- Flexibility: Open source data annotation tools offer more flexibility than commercial and free tools, allowing you to customize them to your needs.
Disadvantages of open source data annotation tools:
- Limited support: Open source data annotation tools may not be as supported as commercial or free tools.
- Limited functionality: Open source data annotation tools may not include all of the functionality you need for ML models.
- Complexity: Open source data annotation tools can be more complex to use than commercial or free tools.
Free data annotation tools
Free data annotation tools are packages that usually come as downloadable apps or executable files. They are very easy to use, but often come with limited documentation and may not offer all the functionality of commercial or open source tools.
Benefits of free data annotation tools:
- Free use: Free data annotation tools are free and often include various features.
- No technical expertise required: Free data annotation tools generally don’t require any technical expertise to use them. This can be useful if you are not familiar with using data analysis and visualization software.
- Largely used : Free data annotation tools, such as Microsoft Excel and Google Sheets, are widely used and can help you communicate your data results to others.
Cons of free data annotation tools:
- Limited support: Free tools may not have as much support as commercial or open source tools.
- Limited functionality: Free tools may not include all of the features you need for ML models, such as data preprocessing and feature selection.
- Complexity: Free tools can be more complex to use than commercial or open source tools because they offer no flexibility to customize them to your needs.
As your needs change over time, the way you make annotations may change as well.
Why change the data annotation tool?
As your needs change, you may find that the tool you were using no longer meets your needs. This can be for several reasons, including:
- The tool is no longer supported or updated.
- It is difficult to learn and use.
- It doesn’t offer all the features you need.
- You need more or less annotation features.
- Your search has changed and the tool is no longer suitable.
How do I modify the data annotation tools?
If you decide that you need to switch to another data annotation tool, there are a couple of ways you need to do it:
- Research your options and potential alternatives in detail.
- Identify the annotation tool that will best meet your needs based on the feature requirements of your documentation and tutorials.
- Estimate how long it will take to learn the new data annotation tool and how much time it can save you by annotating future datasets against the previous tool.
- Check if any licenses or contracts need to be changed when changing annotation tools to avoid unexpected costs.
When deciding which data annotation tools to buy, there are many factors to consider, including quality, machine learning, and strategic approach. To make an informed decision, it is essential to ask the following questions of your data annotation tool vendor:
- What business goals does the toolset intend to help me achieve?
- How do I know if I am using the tool correctly and getting the most from it?
- What kind of return on investment (ROI) can I expect?
- How will my team be able to use the tool most effectively?
- Can you provide proof of concept or pre-purchase trial?
- What features are included in the annotation toolset?
- Are all the required features present or do they need to be purchased as add-ons?
- Are the features clearly explained in the documentation, or are they assumed to be well known?
- Do you offer other services like content moderation?
- How reliable is the annotation toolset?
- How many tests were performed on different datasets?
- What kind of support do you offer if my data is not properly annotated or if I encounter errors while using the toolset?
Currently, machine learning tools are relatively new and their use cases are still being discovered. When choosing a data annotation tool that includes machine learning features, consider the following questions:
- How does this feature benefit me in real life scenarios?
- How does that make my life easier when making annotations?
- What datasets can be used for machine learning?
- Can I see a demo of this feature in action?
- What kind of support is available if I need help using the machine learning capabilities of the annotation tool?
- What expertise do I need to effectively use machine learning capabilities?
Choosing the right data annotation tool is an important decision that should not be taken lightly. By asking the right questions, you can make sure you select a tool that meets your needs and helps you achieve your research goals.