Machine learning algorithms assist organizations in making the best judgments possible. Labeled data, which are meaningful or useful tags applied to raw data samples such as photos, videos, audio, and text, is required for supervised ML algorithms.
Data labeling can be done in-house, outsourced, or crowdsourced, with each option having its own set of benefits and drawbacks. In this post, we’ll go through the various outsourcing possibilities in detail.
- High-quality data is required for effective machine learning models. Despite the importance of good data, identifying and tagging data is the most difficult part of developing ML models. As a result, many businesses prefer to collaborate with third-party data labeling specialists. Outsourcing can help organizations get the most out of their machine learning models.
Advantages and disadvantages of outsourced labeling
To understand whether outsourcing the data labeling process is a good strategic move for your company or not, it must be compared to other typical data labeling procedures, such as in-house and crowdsourcing data labeling.
For data labeling, in-house data labeling makes use of the company’s own data analysts and infrastructure. Crowdsourcing uses internet users as data labelers.
You should compare outsourcing with other options on four dimensions:
- Time required- Outsourcing data labeling saves companies’ time compared to in-house labeling because training a team and building the necessary facilities for the data labeling process are time-consuming activities
- Cost – Because organizations invest less in technology and hire fewer data scientists to focus on the labeling process, outsourcing fared better than in-house data labeling. However, outsourcing is a more expensive data labeling option than crowdsourcing.
- Data labeling quality – Because specialist data labelers operate in these two scenarios, the quality of data labeling is often greater in in-house data labeling. On the other hand, comparing outsourcing and in-house options is challenging since outsourcing businesses may specialize in different aspects of data labeling. Nonetheless, a corporation looking to outsource the data labeling process might identify a data labeling provider that provides good service.
- Safety – Outsourcing data labeling is less secure than doing it in-house but more secure than crowdsourcing. The data is not shared with third parties when a corporation undertakes its own data labeling. As a result, this is the safest labeling method for any business. In contrast to crowdsourcing tactics, outsourcing organizations have certifications and basic security procedures that decrease the risk of data exploitation. There is no way to prevent crowdsourcing employees from revealing your data because they are typically not bound by any security or privacy regulations.
When working with data that isn’t particularly confidential, outsourcing data labeling is a suitable method for a company.
Choose the right provider
If you’ve concluded that outsourcing data labeling is a suitable fit for your company, the next step is to choose the finest supplier. Different service providers give varying features at different costs. As a result, considerable consideration should be given to them.
- Determine the business requirement and set a timetable for it: The first step is to figure out why you need data labeling and how much time your company has to devote to it. This basic self-awareness will assist your firm in eliminating a large number of providers.
- Different suppliers specialize in labeling various sorts of data, including videos, photos, text, and audio. As a result, working with a vendor who has expertise in categorizing the sort of data your company generates is preferable.
- Formats of files: Ascertain that your firm follows the same file formatting guidelines as your selected provider.
- Performance and reliability: It’s critical to determine whether or not the possible suppliers’ data labeling accuracy meets your company’s criteria. You can cross them off the list if they don’t match these requirements.