Do you know that the data annotation market was no less worth than $650 million in the year 2019, and researchers expect it to surpass $5 billion by the year 2026? The reason? Though the myriads of corporates are finding “data labelling service cost” a pricey investment, they have realized the considerable potential in AI data tagging, leading to exponential market growth. Since it is essential in today’s time to automate your core operations to drive down the processing cost, increase efficiency, and generate more enormous profits, you can’t skip the AI implementation anyway. That’s where you need to convert a vast set of raw unlabelled data into training data and use those datasets to train your machine learning algorithms. The benefit? Your ML model will understand how to perform your core functionalities correctly and yield impressive results.
With that covered, it’s time to see whether outsourced data labelling service cost would compel you to break your bank:
Is the price of data labelling too high?
The first thing you need to understand here is that the pricing for data annotation doesn’t get decided based on a single factor. It involves considering data complexity and total volume to reach a final number for the data labelling service cost. Not only that, when you decide the budget for a data annotation project, you also need to factor in the types of labelling you need, for example, images, videos, and text, along with various data annotation techniques.
For instance, whether you want your data to get labelled using the bounding box annotation method or semantic segmentation. Remember, irrespective of the data annotation tactics you use for your project, it is necessary to ensure that the objects in your images are recognizable to machines through computer vision technology for better machine learning training.
Now that you got a critical overview of data labelling cost, let’s move to the third topic:
AI’s 3 New Workforce
One of the most reliable options for data labelling in the market currently is outsourcing. It allows you to partner with a third-party company to get data annotation services. The outsourced AI companies have seasoned annotators that perform data labelling tasks with the highest efficiency. They have the potential to work with a large volume of datasets in a short time.
However, there is a significant drawback to this approach. Outsourcing your data labelling tasks could lead to reduced control over the whole labelling loop, such as custom annotation tools, quality analysis, feedback mechanism, and many more. What else, your communication cost will also surge due to repetitive interactions with the third-party team.
In that case, you need to have a clear set of instructions in place for your potential labelling team. It will help them understand what the task is about and how important it is to annotate the data correctly. Bear in mind that your task requirement could also change the moment your developers start optimizing their AI models in every phase of testing.
Crowdsourcing is a popular term these days that refers to assigning data labelling tasks to individual labellers simultaneously. By following this approach, you can break down large and complex data into tiny parts and distribute it among a large workforce. An unquestioned advantage of using the crowdsourcing method is the lower expenses on data labelling. It is the main reason crowdsourcing is always the top choice for entrepreneurs when facing budgetary concerns.
However, an instrumental thing to note here is that though crowdsourcing is cheaper than other methods, there is always a solid concern related to the accuracy level of labelling tasks. In a recent report, it has come to light that the crowdsourced worker’s error rate is almost 6% for the primary description tasks. And when it comes to sentiment analysis, this rate goes up to 40%.
In the “in-house” model, AI companies hire full-time or part-time data labellers to meet their data annotation requirements. Since the labelling team is part of the company’s personnel group, the developers can supervise the entire annotation process as and when required. Thus, if some projects need special attention, the in-house team can make the adjustments quickly.
So, the fact of the matter is that even though having a separate team of data labellers at your company requires a massive investment, it makes sense to implement this when you have long-term AI projects. The benefit? Your data output will stay stable and consistent throughout the whole data annotation project.
We hope you got deep insights into the data labelling cost and AI’s three new workforces in this blog post. So, if you want to leverage excellent data labelling services right away, contact a premier data annotation company from the comfort of your couch.