Questions You Need to Consider Before Starting a Data Annotation Project

Category: AI insights

Published date: 24.02.2020

Read time: 6 min

While the artificial intelligence field has progressed significantly over the past decade, we have just scraped the surface of its potential. This includes everything from self-driving cars all the way to diagnosing and curing diseases. We all understand that machines require vast amounts of data to learn and notice things that people cannot, the importance is in the types and quality of the data. Let’s not forget that AI is only as smart as the data that is fed into it.

Therefore, in order to train machine learning algorithms, we need to turn regular data into smart data. This is usually done through data annotation services who have to time to go through all of the data and label the things that the machine has to learn. Let’s get a brief overview of what data annotation is before getting into some of the things that are worth your consideration.

Data Annotation

What is Data Annotation?

Data annotation plays a huge role in the overall success of your project because, at this stage, you make sure that the machine has the right information to learn. If you are using data annotation services to help you with this part of the project, it is very important that you have a meeting with the project manager to make sure that they understand exactly what needs to be annotated and the method(s) how it will be done. When you constantly feed properly annotated and labeled data into the machine learning algorithms you are enabling the AI intellect to get smarter over time.

The human factor is absolutely critical at this stage of the project since it will be up to your internal team or data annotation services to classify all of the needed information. Furthermore, given the volume of data that AI projects usually require, you will need to make sure that you have enough human annotators to perform the job and that they remain detail-oriented throughout the project. It is easy to lose focus when annotating thousands of images and other data, therefore you need to have processes in place that protect against this.

Now that we have a definition of data annotation, let’s get into some questions you need to ask yourself before starting your project.

What Needs to Be Annotated?

While this question seems obvious, a lot of companies either do not specify exactly what they would like to be annotated or feel that it would not be possible to annotate what they need in great volume. First of all, you need to remember that data annotation services nowadays offer pretty much anything any data annotation type including:

These are just some of the things that data annotation services offer, so feel free to let your imagination go wild to build revolutionary products. Just be sure that you give clear instructions to the data annotation teams so that the machine learning algorithms will be able to learn what you would like them to.

How Much Data Will You Be Needing?

The answer to this question is usually “As much as we can get our hands on” but it is important that you understand that it will be possible to get enough raw data to satisfy the needs of your project. For example, if you will be needing FAA data over the past decade, make sure that such data will be available. As long as the data that you need is available, the data annotation services will be able to annotate virtually any quantity you need. We do not recommend annotating the data yourself because it is very time consuming and this time would be the better-spent focus on your core business functions.

Do You Need Subject Matter Experts?

If the data that needs to be annotated is very complex, you might need subject matter experts to label it. For example, if you are creating a product for the medical field, you will need people with medical knowledge to label items found on X-rays. Some data annotation services will be able to give you a better rate for hiring medical professionals, especially if you outsource your data annotation needs. Having said this, be sure to consider whether or not you will need someone with industry knowledge annotating your data since this will add to project costs.

Data annotation is a tedious and time-consuming task but, as we said earlier, it is important for the overall success of the project. If you feel that you do not have enough people in-house to annotate the volume of data that you need, there are a lot of data annotation services available to help you.

Posted by Il’ya Dudkin


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