The success of your AI projects is determined by the model performance, which, in turn, depends on the quality of annotated training dataset fed to the model. Data annotation is a time-consuming, expensive, and painful part of any AI project, requiring heavy investments and resources. Many organizations rely on data annotation tools to label and enrich data for training.
A report by Grand View Research suggests that the size of the global data annotation tools market was USD 806 million in 2022, growing at a CAGR of 26% from 2023-2030. One of the benefits of a data annotation tool is that all data attributes are brought to one location, making data definition seamless.
However, there are challenges with data annotation tools, and the biggest of them is the inaccuracy of labeling. For instance, if an image for annotation has a low resolution or multiple objects, data annotation tools will have difficulty labeling it, leading to costly errors and low-quality training datasets.
In partnership with VentureBeat, iMerit recently conducted a study on the challenges of AI projects and roadblocks to AI commercialization. The study found that as AI models become more complex and sophisticated, there is a greater need for high-quality data. One of the most important ways to improve data quality is through precise data annotation and labeling, and annotation tools play an important role.
In this blog, we will look at some of the data annotation technology insights we discovered and some factors to consider when investing in a data annotation tool.
Key Revelations on Data Annotation Technology
- 82% of professionals agreed that scaling annotation efforts would not be possible without investing in both data labeling technology and human data labeling expertise.
- 78% of respondents said finding the tools with the right features or the robustness to handle their data labeling requirements is one of the primary obstacles.
- 45% of companies in the last 12 months have used four or more data annotation tools/platforms, as a single tool could not suffice the requirements.
The State of MLOps Report suggests that data annotation tools are not quite there yet, and organizations rely on multiple solutions to achieve the labeling needs of their AI projects. Also, annotation tools are not sophisticated enough to replace human expertise, and the output from data annotation tools will need supervision from human annotators to improve annotation accuracy.
Identifying the Right Data Annotation Tool
Choosing the annotation tool for your AI project is not an easy decision. Before commencing any AI project, organizations must strategically think about their tooling requirements for the future. Also, choose a tool that meets the project needs, existing and future, while fitting within the budget. We have prepared a quick guide to help with data annotation tool selection.
Volume of Data
It is crucial to ensure that the tool can support the amount of data you have and the file types you need to annotate. A data annotation tool must have extensive features and capabilities for searching, filtering, cloning, sorting, and merging datasets.
File Types
The compatibility of the tool with your file storage systems is equally important. Annotations may come in different formats, including COCO JSONs, Pascal VOC XMLs, TFRecords, text files (CSV, txt), image masks, etc. While it is possible to convert annotations from one format to another, having a tool that can directly output annotations in your target format can significantly simplify the workflow.
Annotation Technique
It refers to the annotation capabilities used to apply labels to your data. Not all tools are the same, as some are optimized for specific types of labeling while others offer a broad range of tools to cater to various use cases. Therefore, choosing one that matches the project’s needs and requirements is essential. The common types of annotation capabilities provided include building and managing ontologies or guidelines, such as label maps, classes, attributes, and specific annotation types.
Features for Better Productivity
While choosing a data annotation tool, looking for features that enhance productivity, save time and improve quality are essential, including a convenient user interface, hotkey support, and others.
Security
The security features of annotation tools, such as secure file access for users and restricted viewing rights for data, are critical. These security measures can help protect sensitive data and prevent unauthorized access.
Quality control
Check if the tool integrates quality control mechanisms in the annotation workflow, like real-time feedback and issue tracking. It may also support labeling consensus and provide a quality dashboard for managers to track quality issues and assign QC tasks to the core annotation or specialized QC teams.
Workforce management
Since we will always need humans to handle exceptions and quality assurance, it is good to consider a data annotation tool that offers workforce management capabilities, such as task assignment and productivity analytics measuring time spent on each task or sub-task.
Conclusion
At iMerit, our data annotation approach is tool agnostic. It means we offer our native annotation tools or can use any client tools or any other 3rd party tools to make data labeling easy, fast, and scalable for our clients. For the future of AI, combining the right technology, talent, and techniques for achieving high-quality data will be the key to success.
The State of MLOps 2023 study demonstrates that companies identifying the right data annotation technology and which lean on domain expertise may achieve successful AI commercialization faster. The tooling industry is not yet mature enough to offer a robust solution for covering the increasingly complex data needs of growing AI projects. To fill this gap, data labeling experts have become crucial to creating the high-quality data required for ML.
View the full report now: The 2023 State of MLOps
