It is an undeniable fact that AI has woven itself and become a facet of our daily lives, thus it drives much of what we do, increasing people’s reliance upon it. Therefore, it is of great importance for AI companies to develop AI algorithms or ML models to serve the increasing needs of the whole society. To fulfill this goal, your company will not only need data to train your model use cases but it will also need high-quality, labeled data at a large number. And that will be the time your business should come to data labeling tools.
Specifically, data labeling tools or software are utilized to quickly and effectively label large volumes of data used for training an AI model. To avoid wasting time or money, it’s essential for your company to choose the appropriate data labeling tool since the AI project’s success or failure may depend on the data annotation tools you use to enhance data for training and deploying machine learning models. However, choosing tools may not be that easy and you will need to spend time as well as lots of effort researching and evaluating to find out the appropriate tools especially when the ecosystem of data annotation tools is rapidly evolving as more suppliers give alternatives for a wider range of use cases.
Therefore, in this article, TagOn would like to provide AI companies with detailed guidelines on choosing the best tools to scale up their data annotation projects. Let’s figure it out right away.
Data annotation tools: definition and types
What are data labeling and data labeling tools?
Before digging any further into how to choose the right data labeling tools, it is necessary to discuss what a data annotation tool is. Firstly, Data annotation in machine learning is the process of classifying unlabeled data including images, text files, videos, etc and then adding one or more insightful labels to provide the data context so that a machine learning model can learn from it and make relevant predictions. When it comes to data annotation tools, it refers to a cloud-based, on-premise, or containerized software solution that can be used to annotate, tag, or label any kind of high-quality training datasets for ML, enabling machines to make human-liked predictions and decisions.
Two main types of data annotation tools
To have the most suitable tools for your data annotation projects, you may need to have an understanding of the two main types of annotation tools, namely Commercial data annotation tools and open-source data annotation tools
Commercial data annotation tools
It must be noted that if your business is in the growth or enterprise stages commercially viable data annotation tools would probably be your best choice. Specifically, if you are scaling up your operations and want to maintain your expansion over time, you can come to get commercially-available tools and modify them with available development resources of your company for better optimization.
Open-source Data Annotation Tools
The source code for open-source data annotation tools is available for use or modification. Therefore, you can modify or tailor the features to meet your specific needs. Developers who work with open-source tools are part of a cooperative user group that can share use cases, best practices, and functional upgrades produced with each other by modifying the original source code.
However, it should be noted that Although open-source tools can be useful for learning or testing prototypes of a commercial application, they frequently pose scaling challenges. This is due to the lack of robust dataset management, label automation, and other efficiency-enhancing features in the majority of open-source tools (like data clustering). Additionally, few open-source tools offer accuracy analytics or quality assurance workflows, which can degrade the quality of data.
As a result, for the best performance of your data labeling projects, it is of great importance to be concerned about the types of data annotation tools.
The importance of data annotation tools for your business
It is a matter of fact that businesses today depend on AI/ML-driven decisions to generate revenues. And one of the most crucial tasks in the training of ML models is labeling the data. It would be extremely challenging, if not impossible, to teach an ML model to implement even simple tasks without labeled data. And to ensure that your labels are accurate, your business will need to find a suitable tool specializing in labeling data that can provide high-quality annotated data, thus helping you get the most out of your machine-learning models.
Which option should you choose: build vs buy data annotation tools
There weren’t many tools for data annotation that could be purchased just a few years ago. If the company wanted to use AI to solve a challenging business problem or develop a disruptive product, the majority of early adopters had to build their own tools or use what was available via open source. However, in 2018, a wave of for-profit data labeling solutions with full features and integrated workflows became available. The emergence of these independent, expertly created tools forced data science and AI project teams to consider whether to stick with the DIY method and build their own tools or buy one.
Therefore, if your companies want to choose the most appropriate tools for their data annotation projects, it is important to know clearly what advantages building and buying can bring to your AI company for further consideration
Reason to build
Even when there are third-party solutions for sale, it might still be advantageous from a commercial standpoint to develop a data annotation tool for their own.
- Control: Specifically, building your own tool gives you complete control over the workflow of the annotation process from top to the bottom as well as the types of data you can label and the outputs that are produced.
- Compatibility: Additionally, you can make changes quickly by using your own developers and establishing your own priorities as you keep refining your business procedures and machine learning models.
- Security: Besides, to satisfy the particular security needs of your business, you can also use technical controls.
- Incorporation: Moreover, developing a data annotation tool internally enables a company to incorporate all of its AI technology into its intellectual property.
Reason to buy
In general, as your project or product evolves, buying data annotation tool can be beneficial to your project in some ways:
- Less expensive: Purchasing a data annotation tool that is already commercially accessible is likely to have a more reasonable price since you can cut down the cost in upfront investment and of maintaining and enhancing features and capabilities for a built-in tool, enabling you to spend time and resources on the core task that can create profit for your companies.
- Operating quickly: Moreover, by purchasing an existing data annotation tool, your companies can accelerate their project timeline.
- Flexibility: Finally, you can usually customize a commercial tool’s functionality to suit your needs
What to consider when choosing data annotation tools?
After considering all the above factors, what your companies need to do now is create suitable criteria for choosing the most appropriate data annotation tools. If you need help figuring out where to start, TagOn will help you by providing some of the factors you should focus on.
What is your company’s use case?
Your choice of tool will primarily depend on the kind of data you want to annotate and the business procedures you will use to complete the task. Specifically, you will need different tools to deal with different types of data including text, images, etc. In addition to the annotation capabilities, you should think about data security certifications, needs for quality assurance, storage alternatives, and labeling features like polygons and bounding boxes, among others. Therefore, it would be helpful if you can take your company use case, and the specific needs, and requirements of your project as it may offer more flexibility for your tool in the future.
What are the key features of data annotation projects?
An extensive method of managing the dataset you intend to annotate is where annotation starts and finishes. You must confirm that the tool you are contemplating will actually import and support the large volume of data and file formats you need to label because it is an essential component of your process. These operations on datasets include searching, filtering, sorting, cloning, and merging.
Moreover, since different tools can save the output of annotations in various ways, you must ensure the tool will satisfy the output needs of your company. After that, what you need to do is save your annotated data.
Data quality control
Clearly, your data quality will determine how well your machine learning and artificial intelligence models perform. Therefore, it is important to select a tool that can assist in managing the quality assurance (QA) and verification processes. And the most ideal data annotation tool is expected to incorporate quality control into the annotation process itself.
A human workforce is intended to employ a data annotation tool. To handle exceptions and quality assurance, you still need people. The most effective data annotation tools will include workforce management features like task assignment and productivity analytics that track how much time is spent on each job or subtask.
Another feature that you may need to seek for when choosing data annotation tools is their security. Specifically, Tools should restrict annotators’ access to non-assigned data and forbid data downloads. Moreover, a data annotation tool should provide secure access whether it is used on-premises or in the cloud.
What are the criteria for choosing the appropriate tools?
Efficiency and Functionality
It is obvious that data labeling requires could take up a significant amount of time and resources. Therefore, it is essential to find tools that will make hand annotating as quick as possible. We can save time and get better annotation quality by using features like a comfortable user interface (UI), hotkey support, and other capabilities to increase efficiency. Moreover, to optimize your data annotation tools, you had better find one that offers all the features you require.
The methods of annotating data are undoubtedly the main features to consider when choosing data annotation tools. Specifically, there are many tools that are offering a wide variety of capabilities to support all types of use cases, others are more tightly focused to concentrate on particular types of labeling. Moreover, when choosing the methods for annotating, you may need to focus on an emerging feature in data labeling technologies, namely automation, or auto-labeling since it is expected to help annotators make better annotations.
Ability to meet the demands on time
The ability of any data labeling system to work within your timeframe is another issue that you should discuss before buying. You should be able to receive your high-quality, correctly labeled data on time and according to your timeline otherwise, your work will be delayed, thus damaging your projects.
It is a matter of fact that price is always among the most considerable factors for AI companies, especially for SMEs to be concerned when selecting a data annotation tool. If your companies do not want to face the financial burden later, they will need to carefully consider the price factors and even prepare a budget for the hidden cost to avoid falling into cash shortage.
Top recommended data annotation tools for your company
TagOn is a scaling-up data ecosystem for AI data labeling. It provides advanced Annotation tools for data labeling and validating and collecting across 16 types of data projects.
TagOn reduces the time for labeling & operating projects 10 times with an efficient working system. Not only completes a project with significant time reduced but also guarantees a minimum accuracy of 95% of the gold standard. With this remarkable ability, TagOn ensures your work will be an easy success. TagOn offers a diverse and skilled team that includes both face-to-face and online divisions. They can manage and maintain lots of employees for each data project. You will no longer be concerned about project delays due to a shortage of personnel.
Therefore, TagOn is expected to be one of your best choices when it comes to data annotation tools.
Labelbox is a training data platform comprising three core layers that streamline the entire process, from labeling and collaboration to iteration.
Labelbox is the only solution that combines all data labeling tools and services, model-assisted labeling, unsupervised learning, semi-supervised learning, and weak supervision approaches, as well as many other features. It is an end-to-end software platform that enables AI/ML teams to produce and manage high-quality training data in a secure environment.
V7 is an automated annotation platform that combines dataset management, image annotation, video annotation, and autoML model training to complete labeling tasks automatically. They develop software to assist organizations in automating any visual process. Their goods are not only speedy but also pixel-perfect and simple to use.
With Auto-Annotate in V7, your project productivity will increase. Auto-Annotate uses a deep-learning model to segment items and produces pixel-perfect polygon masks automatically. By operating the basic steps, Auto will create precise outlines around the item. It’s intended to save you a lot of time, and the accuracy is also increased.
To conclude, it can be said that in order to choose the most appropriate data annotation tool for your labeling projects, you need to thoroughly understand and be well acquainted with the basic information of the tools. However, with these detailed guidelines, we hope that you now can know where to start, and which steps to take to successfully select your own tools. And if you need any further information on the data annotation tools and their application, you can follow this link to visit our website, which brings you all the updated news on data annotation and AI https://tagon.ai/
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