Technology and AI are rapidly taking over daily activities, with proper use of data having a significant impact on modern society. Machine learning (ML) techniques call for high-quality annotated data to detect issues and propose workable solutions. Data collection, data preprocessing, and data labeling are three crucial factors determining an ML project’s quality.
If you have significant amounts of data for ML or deep learning, you’ll need the right data labeling platform and experts for your project. Data labeling is time-consuming and complex, as you have to wade through immense amounts of unstructured data. Getting the data labeled accurately requires a lot of patience, time, and organization. Many low-quality datasets will prolong the labeling process, making it harder to achieve an ideal ML model. Datasets can contain thousands of samples that need labeling; thus, having high-quality data saves you time and money from lower labeling costs.
In addition, data labeling entails extreme attention, as each mistake or inaccuracy can negatively affect the dataset’s quality and the AI model’s overall performance. You have to consider the complexity of the task, its size, and its timeline when choosing the right approach to implementing data labeling in your ML project. Read on to learn about the standard methods of labeling data.
Data labeling approaches
There are five main approaches to data labeling: in-house, outsourcing, crowdsourcing, synthetic labeling, and programmed labeling. Each method has its strengths and weaknesses, and knowing which is best depends on various factors. These factors include:
- The complexity of your use case
- The training data required
- The size of your company
- The size of your data science team
- Your finances
- Your timeline
For instance, in-house labeling will be suitable for companies with great resources, while crowdsourcing labeling is ideal for those with limited resources.
- In-house labeling
Data labeling can be done in-house by an internal team of data scientists. This internal approach to labeling ensures the highest possible level of accuracy, and you can efficiently track the process and have predictable results.
In-house data labeling may be a slow process, but it’s a good option if you have sufficient human resources, time, and finances. Industries like healthcare and insurance require a high level of accuracy. Therefore, internal data scientists must consult experts in the corresponding fields for proper data labeling.
If you have a set timeline for the project, then outsourcing data labeling services is the way to go. With proper planning and organization, you can create a temporary labeling team from interviewees with the appropriate skill set. The new staff will require training to meet the new job’s requirements and complete it as per specifications.
In short, outsourcing to an individual organization is a viable option if you don’t have sufficient funds for quality data annotation.
Crowdsourcing involves enlisting the help of individuals from across the globe to handle particular tasks. This method is a swift and cost-effective way of implementing data labeling since tasks are performed immediately and available by anyone worldwide.
Crowdsourcing platforms offer you many workers at once, so you don’t have to spend much time recruiting people. When searching for a crowdsourcing partner, you’ll need to take some factors into consideration, such as quality. Does the company you’re assessing qualify the people who will be in charge of labeling your data? What kind of quality control processes do they offer as a contingent against data that’s labeled inconsistently?
Though crowdsourcing is a practical avenue for companies that can’t afford an in-house annotation workforce, it comes with limitations because quality assurance, worker quality, project tools, and worker management vary wildly.
- Synthetic labeling
Synthetic labeling involves creating or generating new data containing the necessary attributes for your project. It enables you to create new data from existing datasets.
Additionally, it uses generative adversarial networks (GANs) to create fake data and differentiate it from actual data. GAN uses two neural networks to generate and discriminate data, resulting in a highly realistic new data set. They’re time efficient and ideal for producing high-quality data but are costly, as they require a lot of computing power.
- Programmed labeling
Programmed labeling automatically labels data using scripts. This process eliminates the need for numerous human labelers since a staff member can automate tasks, such as text and image annotation. Computers are also much faster than humans, as they don’t need to rest, but the results might be far from perfect. Plus, programmed labeling works along with a team of quality assurance staff that reviews the labeled dataset.
Data labeling is the main challenge hindering large-scale AI integration into industries today. Careful and accurate data annotation is a fundamental part of the success of any ML project, which is why it’s always in high demand.
There’s a wide variety of annotation tools online, and it’s often difficult for the data science team to determine which will work best for a specific project. However, using both external and automated data labeling is well-recommended.