More companies are embracing machine learning (ML) and artificial intelligence (AI) to automate decision-making and help drive new business opportunities. However, one persistent problem is that AI algorithms are like babies; they have no idea what anything is unless we tell them.
Enter data labeling. You can ensure your machine learning and AI projects are trained with accurate information by labeling the data, which is also referred to as data annotation. Over time, you can create a more intelligent model by regularly feeding tagged and annotated datasets into an algorithm.
Data labeling may seem simple, but it isn’t always easy to implement. Getting it wrong can end up disrupting the entire model training process. To determine the right approach for labeling and develop a strategy for scalable implementation, companies must consider many factors and methods.
Employ model-specific labeling techniques
Annotators can use various methods to add the necessary information to collected data. However, choosing the suitable labeling method is a high-risk decision for teams because it is not always apparent what type of data annotation is needed.
Take into account what you will be training the model for when choosing a labeling technique. These are some standard techniques for labeling data, including:
- Image classification: The purpose is to train a model to recognize the existence of an object in an image. For instance, does the image contain a ball or not? Does the image contain any object at all, whether a ball, a cup, or a shoe? The limitation is that it oversimplifies an image into one label and misses other details that can add context to an image. Also, it only teaches a model to recognize an object in an image, not the object’s position in the image.
- Object detection: Object detection defines the existence and location of objects in an image. This technique trains a model to recognize, locate, and count objects in unlabeled images. But it doesn’t clearly define the size and shape of an object, e.g., if it captures a sitting dog during training, the model might have a problem capturing the same dog if the dog is jumping or standing.
- Sentiment analysis: Sentiment analysis is a text classification technique that analyses a body of text and determines if the underlying tone in the text is positive, negative, or neutral. However, the model might struggle with grey areas such as texts that involve sarcasm or irony.
There are multiple labeling techniques with unique use cases they cater to. Speaker diarization, for example, is used to partition audio into segments according to the speaker’s identity. The list goes on and on. However, the end goal is to ensure that the labels perfectly match the reality where the model will be utilized. Choosing the wrong technique can completely alter the model’s training.
Hire/Use in-house human experts
Data labeling requires human expertise. Companies can use automation to reduce human disadvantages, such as human error, but the advantages provided by human data annotators outweigh the disadvantages. Human minds are not rigid structures like machines. They offer better precision and accuracy in data annotation projects, especially when analyzing sentiment and emotion.
Having an internal data labeling team is beneficial to accurately training models. They can directly oversee the whole data annotation process and leverage internal domain expertise to annotate with higher accuracy. When data labeling tasks include sensitive information companies cannot send over the internet without breaching security standards, having an internal labeling team is the right solution.
In long-term machine learning projects where data is being sent back and forth continuously, having an internal labeling team is more efficient because the annotators will be needed close to other team members to label the data constantly. Additionally, it will be easier to diagnose and communicate solutions when something goes wrong.
Here are some basic skills your in-house team should have:
- Attention to detail: Missing some parts of the object, incorrect tagging, or labeling more or less of the object may jeopardize the model’s training. A data annotator should be able to locate the tiniest of details in an image.
- Focus for prolonged periods: Data labeling requires perseverance. A data annotator needs to stay focused on what is happening on the screen without getting distracted and making mistakes.
- Commitment to privacy: Before allowing a labeling team to begin any labeling, you should ensure that they are willing to sign Non-disclosure Agreements (NDAs) before providing sensitive data.
Establish a quality assurance process
People will inevitably make mistakes—mistakes are one of the mechanisms that allow us to learn and improve. The inclusion of quality assurance (QA) is crucial to avoid mistakes in data annotation. We can ensure that labels are being made correctly and that any errors are detected and fixed before being used in model training.
You can conduct a QA check by regularly auditing data labeling tasks, including examining the data labeling process from start to finish and bringing in subject matter experts to check the accuracy of the labels. Compare the annotations in your dataset with an ideal set of annotations to verify that your model accurately reflects the real-life conditions in which you plan to use it.
Measuring your labels’ consistency is also important. Adding labels consistently means that everyone in your team is adding them the same way. If your labels are inconsistent, there will be a lot of confusion in the data set. For example, labeling helicopters as “helicopter” and “chopper” in the same data set can confuse the model. Let multiple annotators label the same samples and use agreement matrices to identify and resolve labeling issues.
Also, test the quality of each annotator’s work regularly by randomly selecting a sample to review. Take notes of common mistakes and adjust your labeling guidelines based on the results.
Select a flexible data labeling tool
Tool choice is an essential factor in designing, testing, and deploying an ML model. It is also an essential factor in determining the success of a data labeling project. Your labeling needs (text, audio, video, etc.) will determine which tools are available to you.
When choosing a tool, consider both your data’s size right now and its projected size as you grow. Make sure your choice data labeling tool is scalable as the machine learning model improves and requires a larger volume of quality data. For instance, the tool you use should support adding third parties when necessary to support surges in labeling needs.
Ensure that your data labeling tool supports vital QA functions that allow you to identify issues and automatically share them with your teammates quickly. If you work with a 3rd party data labeling provider, you will likely share your sensitive or personal information with them. You should evaluate a provider’s security practices before choosing one for your needs.
Many tech companies might use AWS, Google Cloud Platform, or Azure for their infrastructure, specifically their data storage. Your labeling tool should integrate seamlessly with these tools and every other tool that data science teams will likely use. Additionally, the labeling tool should support webhook events, so users can be alerted when important things happen on the platform.
Heartex’s data labeling tool, Label Studio, supports various data types, including audio and video. It integrates seamlessly with a vast range of tools and ensures adequate data security alongside.
Be one step ahead with a data labeling strategy
Building a data labeling strategy is more than planning how annotators will come on board and label gathered data. It is an in-depth process that keeps you one step ahead of errors and pitfalls affecting a data labeling project.
Data labeling is a rigorous process that takes time and resources, both human and technical. Having a strategy in place helps you approach it quickly. While companies might approach data labeling in varying methods, the points discussed here help to reduce errors and risks and improve labeling quality while saving costs in the long run.