How to Choose the Best Data Annotation Tool for Machine Learning?

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The number of useful applications powered by machine learning (ML) is growing rapidly. These applications range from simple robotics to autonomous driving and complex recommendation systems that power services like Netflix or Amazon algorithmically predict the products we might like to buy next.

But building an excellent ML-based application isn’t just about using state-of-the-art algorithms and training them with lots of data – it’s also about having high-quality data for training those algorithms, i.e., making sure there aren’t errors in the dataset that can lead to flawed conclusions or inferences.

This blog post will introduce the concept of data annotation toolkits and provide a detailed description of how these tools can help you improve your dataset quality.

Build Versus Buy

When it comes to data annotation, there are two main options: build or buy. You create or use an open-source toolkit to do the annotation work yourself in the build scenario. You would purchase a commercial toolkit that does the annotation work for you in the buy scenario. Let’s take a look at each of these scenarios in more detail.

When to build your data annotation tool?

There are a few cases where it might make sense to build your own data annotation toolkit:

  1. You need a particular type of annotation not available in any existing toolsets.
  2. You require annotation to be done quickly, or you need real-time annotations.
  3. You need the annotations in a specific format.
  4. The annotation tool needs to integrate with existing data pipelines.

If any of these points apply to your use case, building your toolkit might make sense rather than purchasing one off the shelf. However, building an annotation tool is not trivial and will undoubtedly take time. Using an open-source solution can significantly reduce the effort required to start data annotation.

When to buy a data annotation tool?

If none of the points above apply to your use case, then chances are that it probably makes more sense to purchase a commercial data annotation toolkit. Let’s look at some of the factors you should consider when making this decision.

  1. How much data do you need to annotate? If you deal with a large volume of data, buying a toolkit might make more sense than building your own. This is especially true if the annotation process needs to be done quickly or in real-time.
  2. What type of annotations do you need? Most commercial data annotation toolkits support various annotations, including text, image, and bounding box.
  3. How well does the toolkit integrate with existing systems? It’s vital that the annotation tool you choose can be easily integrated with your existing data pipelines.
  4. What is your budget? If price is the only consideration, it makes sense to buy a commercial toolkit rather than build one yourself.

The open-source option for data annotation tools

Many open-source alternatives are available for building your own data annotation toolkit.

While building your annotation system might make sense if you have particular functionality or workflows that aren’t supported by existing tools, we generally recommend that you purchase a commercial toolkit if none of the points outlined above apply to your use case.

Growth stage as an indicator for buy vs. build

In evaluating which factors are most important to your specific use case, it’s also important to consider your business stage when making this decision. We have found that small companies and startups prefer building their toolkits. At the same time, large enterprises usually opt for purchasing a commercial product.

However, there are certainly exceptions on both sides where a company might choose not to go with the obvious choice. Here is some more context on how the growth stage can impact the decision process:

  1. Startups and early-stage companies: In the early days, a company is typically trying to do everything themselves to save money and grow as quickly as possible. Building your own data annotation toolkit is a natural extension of this mindset. As the company grows, it will likely need to focus on its core competencies and outsource other tasks such as data annotation.
  2. Late-stage companies: By this point, a company has usually figured out its core strengths and is looking for products and services that can help it quickly scale. Purchasing a commercial data annotation toolkit is often seen to speed up the annotation process and get products to market faster.
  3. Mature companies: These companies tend to have well-established processes and workflows, which mitigates the need to build their annotation tool. Mature companies are also more likely to start hiring data scientists, increasing the likelihood of building their toolkits.

How to Choose a Data Annotation Tool?

Defining your exact use case is one of the essential factors you should consider when deciding whether or not to build your own data annotation toolkit. This section will provide some additional context for users who fall into these categories:

  1. The type of annotations they want.
  2. Who will be annotating?
  3. How quality control requirements might change over time.
  4. If they have a budget limit in mind.

What is your use case?

  1. What type of annotations do you need? Many different annotations can be helpful for machine learning tasks such as text, image, and bounding box annotations. It’s essential to decide which annotations are the most important for your use case and prioritize those when making a decision.
  2. Who will be annotating? If you’re thinking about building your annotation toolkit, it’s essential to think about who will be doing the annotation work. Will it be employees within your company, or will you outsource the work to a third party? Outsourcing can be a great option if you don’t have the manpower or resources to do the annotation work in-house, but finding a suitable partner can be more expensive and time-consuming.
  3. How will quality control requirements change over time? As your data set grows, the need for high-quality annotations will also increase. This is especially true if you plan on using machine learning algorithms to make predictions or models. You’ll need to have a system in place to ensure that all annotations meet your quality control standards. This could involve having a consensus process among annotators, using a gold standard dataset, or doing sample reviews.
  4. What’s your budget limit? One of the deciding factors for many people when choosing a data annotation tool is how much they’re willing to spend. Many commercial options range anywhere from $5/annotation for low-end to enterprise options that cost over $100,000. Building your tool is also possible, but it will take more time and effort than simply purchasing a commercial product.

How will you manage quality control requirements?

Quality control is essential for data annotation for machine learning algorithms. It can be beneficial to have consistent guidelines in place so that annotations are consistent across the entire dataset. Still, it’s important to remember that consistency doesn’t necessarily mean accuracy (and vice versa).

If you’re investing in building your toolkit, then many different factors can affect how accurate your annotations are, including:

  • The annotation interface.
  • The people annotating.
  • The gold standard dataset that you’re using.
  1. Consensus: One way to ensure high-quality annotations is by having a consensus process among all annotators. This involves everyone agreeing on the definition of what an annotation is and how it should be marked. Having a clear and concise annotation format can help with this process.
  2. Gold standard: A gold standard dataset is a set of data used as a benchmark for measuring the accuracy of other datasets. It’s essential to use a gold standard dataset when validating or to compare work.
  3. Sample review: Another way to assess the accuracy of annotations is by doing sample reviews. This involves selecting a small number of annotations from your data set and comparing them to the corresponding annotations from the gold standard dataset. By doing this, you can understand how well your annotation process works.
  4. Intersection over Union (IoU): The intersection over union (IoU) metric is a way of measuring the accuracy of two sets of annotations. It considers both the number of annotations shared between the two sets and the size of the intersection (the amount of overlap between the two sets). This metric is often used when evaluating the accuracy of image annotations.

Conclusion

When choosing a data annotation tool for machine learning tasks, there are many different factors to consider. It’s essential to think about what type of annotations are needed, who will be doing the annotation work, and how the quality control requirements will change over time.

The right tool for the job will depend on the project’s specific needs. There are many commercial and open-source options available, so there’s sure to be something that fits the bill. Thanks for reading!

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