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A Quick Guide To Data Labelling Automation

Last updated on July 14th, 2022 at 03:08 am

Completing an AI project is no joke. It requires unimaginable amounts of data that ML practitioners must collect, curate, label, and feed into the ML model. 

Data labelling, also known as data annotation, is time-consuming. It’s the method of attaching a label to all training data, which may come in images or videos. Since data labelling must be as precise as possible to ensure better model accuracy, you must not tolerate errors. For these reasons, practitioners often seek the help of human annotators. But that might not be as necessary with the introduction of data labelling automation. 

What is data labelling automation? 

Data labelling automation is the practice of automating certain parts of the data annotation/labelling process. But contrary to common belief, it’s impossible to automate the process entirely. A human must still be present to monitor the automation and ensure the highest possible accuracy. More specifically, it requires human-in-the-loop (HITL). This term refers to the constant validation and supervision of an ML model’s outcomes by a human. 

These practitioners ensure metrics are satisfactory. A video annotation tool is an excellent example of data labelling automation that requires HITL. It’s a tool that can detect valid objects in a video and attach labels to each object accordingly, but it still requires user input. 

The significance of data labelling automation 

Implementing data labelling automation is no joke, as convenient as it may be. It can take several hours or even days if the dataset is large. Furthermore, tools that make it possible can add to a project’s expense. However, ML practitioners prefer to start projects with data labelling automation than without it. This is because of the benefits it brings to the table. 

Below are three examples of these supposed benefits: 

  1. Superior speed 

One of the bottlenecks of machine learning projects is that it takes too long to complete preparations. According to a study by Cognilytica, preparation takes up 80% of the time spent on a typical AI/ML project. Of this 80%, 25% is data labelling, which most will consider high. 

Data labelling automation solves this by decreasing the time it takes to label objects, which can pile up to several days’ worth of time with a large dataset. In other words, you can accelerate the project’s timeline considerably with data labelling automation.

  1. Lower costs 

Labeling a single object takes around 10 seconds, and an image often consists of five or more objects. Assuming an image consists of six shots, it’d take a minute to label completely. A typical dataset with 100,000 images would equal 1,666 hours of work per dataset. So if you hire an annotator for USD$6.00 per hour, you’ll have to spend around USD$10,000 per annotator. 

This figure is too much, especially since this expense is only for just data labelling. As discussed in the previous benefit, automation reduces this cost by improving speed.

  1. Higher Accuracy 

In AI, there is a metric called confidence level. It corresponds to the likelihood or probability that a result is accurate. It applies to many things, including data labelling automation results. 

If the confidence level is high, the labelling is more likely to be correct. Similarly, if it’s low, there’s a good chance the tool mislabeled the object. Though confidence levels can sometimes be low, it’s often not an issue. When the automation tool encounters a result with low confidence levels, it can send those to a human annotator for correction.

With this system, you can reduce the likelihood of mistakes by seeking a second result from a different medium—humans. In turn, this system maximizes the accuracy of the AI/ML model. 

Best use cases of data labelling automation 

Although data labelling automation is a powerful technology in general, there are cases where it shines the most. More specifically, there are two best use cases of the technology. 

Let’s take a look at each one: 

  • Models with a lot of data: Data labelling automation shines the most in models that handle a lot of data as it minimizes your expenditure. It also speeds up the project. 
  • Models that need frequent revisions: Automation is ideal for models requiring regular updates. An excellent example of this is self-service retail stores.

Packaging designs change, banks update credit card designs, and the store undergoes renovations. To adapt to these changes, the data labelling technology the self-service retail store uses must receive frequent updates. Though it might be difficult with human annotators, it should be more manageable with data labelling automation.

When is data labelling automation least applicable? 

Like there are best use cases of data labelling automation, there are also instances where it can bring more harm than good to the AI project. These instances are the opposite of the aforementioned best use cases. For example, it’s not suitable if the model has a relatively small dataset or if it doesn’t have a significant model drift, a specific metric in AI projects. 

Model drift refers to the rate at which an ML model becomes less accurate as time passes due to changes in data or variables. At some point, it can become obsolete due to model drift. 

The AI technology of a self-service retail store is one example of a model with considerable model drift since it can become obsolete given time. But those with no significant model drift are examples of models where data labelling automation would be least functional. In these models, not only do frequent revisions barely lead to any improvement, but it also necessitates a more significant number of quality assurance (QA) professionals. This can then result in more expenses.

Takeaways 

Data labelling automation is undoubtedly a powerful technology. It can accelerate a project’s timeline, save the project managers a considerable amount of money, and maximize the model’s accuracy. For these reasons, data labelling is an ideal tool in models with large datasets and those requiring frequent updates. But as always, it’s not always going to be as helpful as you think. That’s why you must assess your AI project if it truly needs data labelling automation.

References

Heffelfinger, Bill. “Top Benefits and Limitations of Auto Labeling.” CloudFactory Blog, April 29, 2022. https://blog.cloudfactory.com/top-benefits-and-limitations-of-auto-labeling.

Lasorsa, Caroline. “An Introduction to Automated Data Labeling.” Superb AI. https://www.superb-ai.com/blog/an-introduction-to-automated-data-labeling.

Shendre, Sushrut. “Model Drift in Machine Learning.” Towards Data Science, May 14, 2020. https://towardsdatascience.com/model-drift-in-machine-learning-models-8f7e7413b563?gi=383f9f4fe5bd.

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