4 things every tester should know (2020 Video Series)
This week, we start a new video series on AI Data Labeling Tool. This is the first video about “5 important Data Annotation Tool Features”: Dataset Management, Annotation Methods, Data Quality Control, Workforce Management and Security.
You can watch our video here, or read the transcription below. Turn on subtitles for English, Japanese, Korean and Vietnamese.
You are…
- Beginning a machine learning project and have data you want to clean and annotate to train, test, and validate your model?
- Working with a new data type and need to understand the best tools available for annotating that data?
- In the production stage and must verify models using human-in-the-loop?
If Yes, this video is for you.
Hello guys, I’m Joyce and this is Lotus QA channel.
Welcome to our new video series in which I am going to walk you through most of the aspects that you need to know about the data annotation tools:
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- Key features
- Buy or Build?
- How to choose
- Some suggestions
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So, what are you waiting for? Do you want to have a good annotation tool? Let’s start with this 5 important data annotation tool features.
What’s a data annotation tool?
Before we outline some features, let’s first define what annotation tool is?
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. While some organizations build their own tools, there are many data annotation tools available via open source or freeware.
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.
Finally, your annotated data must be stored somewhere, so confirm support-file storage targets.
Annotation methods
Obviously, this is 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. If you want to check more about image annotation types, you can check this video.
Moreover, an emerging feature in many data annotation tools is automation, or auto-labeling. Using AI, many tools will assist your human labelers to improve their annotations or even automatically annotate your data without a human touch. Additionally, 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 labelers 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.
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 is 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.
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. As such, leading tools will offer workforce management capabilities; such as task assignment and productivity analytics measuring time spent on each task or sub-task.
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).
So, those are 5 key features that I want to talk in this video.
If you find a great one that we didn’t mention, please tell us in the comments and I hope this video will help your team grow.
Don’t forget to like and subscribe our Youtube channel if you want to see more. Bye for now.
Our next video in the series is coming soon. You might also want to check out our series on Data Annotation Essentials.
Interested in our AI Data Annotation Service?
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