A Guide to Choosing Your Data Labeling Tool

Data Labeling Tool

Machine learning and deep learning require tools and people to enrich large volumes of data for model training, validation, and tuning. If your team is like others, it handles most tasks internally and would like to free up more time to devote to strategic planning. Ready to outsource your data labeling?

To help you outsource this important but time-consuming task, this guide will walk you through the steps you need to follow.

Based On Your Needs, Narrow Down Your Options For Tools

Naturally, the tools at your disposal will vary according to the data you need to classify. Text, image, and video data tagging tools exist. Video labeling is an additional feature offered by some picture labeling programs. Annotation functions, quality assurance (QA) features, file formats supported, data security certifications, and storage options are just a few of the many ways in which tools diverge. Some examples of labeling features are bounding boxes, polygons, 2-D and 3-D points, semantic segmentation, and others. To ensure that your machine learning model has access to high-quality input data, you must first determine what features are necessary for your specific use case and the domain in which your model will be deployed.

The workforce faces new hurdles with each new data type. When classifying texts, data workers must often have a thorough understanding of the surrounding situation and the confidence to make consistent, accurate, and subjective judgments across the entire dataset. Image labeling might be more complex than categorizing text or numbers since it often requires additional information. Growing a video tagging operation on a large scale is far more complex. The average frame rate for a ten-minute film is between thirty and sixty frames per second, so a total of eighteen thousand to thirty-six thousand frames is contained in the footage. To be as accurate as possible, frame-by-frame video labeling is a time-consuming and specialized talent that benefits from direct instruction and practice. Combining automated tools with human labor can accomplish this more quickly and with less effort.

Plan For Supply Of Labour Force Size

Using a data labeling service, you can tap into a sizable labor force. Crowdsourcing can be done as well, albeit data quality was shown to be poorer when compared to managed teams for the same data labeling activities, according to a study by data science technology firm Hivemind.

The quality of your data will improve over time as your labelers become more acquainted with your business rules, context, and edge cases if you stick with the same team. When new team members are brought in, they can also be trained. As quality and the ability to iterate are very important in data labeling for machine learning applications, this is highly useful.

Look for elasticity

Check for labeling that can be scaled up or down easily. Because of the constant stream of data, it may be necessary to do labeling in real-time. For example, like many other businesses, yours may experience a surge in sales in the weeks leading up to the major gift-giving holidays. We also discovered that releases of new products might cause a surge in the amount of data that needs to be labeled. A staff that can scale up or down depending on demand is ideal.

Opt for savvy tools

Scaling data labeling will depend heavily on the data enrichment technology you decide to use, whether you buy it or design it yourself. It’s important to remember that this is an ongoing procedure; the data labeling tasks you’re working on now may require a different approach soon, so you shouldn’t make any decisions that would commit you to a course of action that might not be optimal.

You’ll need a solution to tweak data features, your labeling procedure, and your data labeling service as you expand or scale. Compared to custom-built tools, commercially-available tools provide more flexibility in workflow, features, security, and integration. They also allow for some wiggle room in case plans need to be adjusted.

Make sure your data labeling team is in the loop with the rest of the project’s progress.

Effective, easily accessible communication with your data labeling team facilitates scaling. To make rapid, far-reaching adjustments, such as modifying your labeling procedure or iterating on your data’s features, we advise keeping a tight communication loop with your labeling team.

When the accuracy of data labeling is crucial to the functionality of your product or the quality of the experience it provides to customers, you can’t afford to wait for a response. Providers of data labeling services should be ready to accommodate clients in different time zones and adjust their communications accordingly.

Conclusion

Data labeling is the process of assigning labels to data. It is used to categorize and organize data, making it easier to find information.

It can be time-consuming and error-prone if your team involves a lot of people, but Springbord provides the best data labeling services that enable your team to quickly label large datasets with minimal human effort.

Related Posts

Begin typing your search term above and press enter to search. Press ESC to cancel.

Back To Top