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: 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.
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:
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:
What is your use case?
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.
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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