What is data annotation?

It's adding an explanation or label to a piece of data to categorise it.

Data annotation is exactly what the name suggests, it's adding an explanation or label to a piece of data to categorise it.

A simple example of this would be deciding if a picture contains a dog and labelling it, like the captchas you do when a website is trying to verify if you are a robot.

What is it for?

Computers cannot process information the way humans do. While it may be easy for a human to identify a dog in an image, a computer sees an image as 0s and 1s, and cannot comprehend what an image contains. We use context, surrounding circumstances, and our past experiences to inform us and help us to fully understand, evaluate and interpret the subject in an image.

However, for computers to comprehend, they need some help - by being provided with this exact context. Labelled content provides that context and can then be used and recognised by computer vision and machine learning models and used to make predictions.

Data annotation Techniques!?

• Usage-Based Data Annotation

Ideally, data samples are associated with the labels organically, in which case data annotation is not required. This can happen when there is a well-defined business process that generates data.

For example, the manufacturing industry usually has a QA Department that will check defects and quality of the product. During the long term, they will have a large database of approval and rejection of the product.

This data can be used to train a machine learning scoring model. Data samples include the reason of defect, product type, rejection notes, and so on, and the corresponding data labels are binary decisions for the QA department to build the standard in the future.

• Data-Driven Data Annotation

In many AI projects, you can define simple rules that are capable of solving the problem for a subset of the data. If that subset contains a representative sample and has sufficient quality, you can collect enough data sample-label pairs to train a machine learning model with the high generalisation ability to the entire data set.

• Manual Data Annotation

During the initial phases of an AI project, such as when the data sets are small or the goal is to quickly build a prototype, you can annotate a data set manually. In this case, developers working on a project review the data and put labels on the data samples following the annotation guidelines.

• Using Data Annotation Service

Some platforms that help you with data labelling service to get high accuracy data to help build your A.I. and machine learning models and support most types of data annotation.

Tictag provides an innovative and excellent solution to this exact problem.

What is good data annotation?

Data quality is very important in a Machine Learning models’ performance, and can make or break it. But what are the qualities of data that have been annotated well?

• Completeness: A small, incomplete dataset may under-represent the context. Having all the necessary and appropriate parts is important to ensure that the provided context is not skewed.

• Accuracy: A common phrase used in the ML community is “Garbage In Garbage Out” which means that the models’ quality is very much dependent on the quality of data

• Availability: In the ever evolving AI field, as more complex machine learning projects are being developed, more complex and unique datasets will need to be created. As such a good dataset should be quickly available

Why is good data annotation important?

Data is the lifeblood of assisted machine learning projects. The more data you have, the more accurate the end-product will be. However, it is not simply enough to have raw data. You need to have this data annotated so that the machine learning algorithm can properly identify the objects in a given image, understand human speech, and many other functionalities.

Because of that, we can see the correlation between correctly annotated data and the success of the project. However, this is also supported by research since according to some estimates, 80% of AI project development time is spent on preparing the data. The reason data annotation is so important is that even the slightest error could prove to be disastrous. As humans, this is one of the areas where we have a leg up on the computers since we can better deal with ambiguity, decipher the intent, and many other factors that go into data annotation.

Data Annotation Platforms!?

There are several data annotation platforms available to solve your data labelling and preparation needs since human data labelling is very important for building A.I. and machine learning models. One of which is Tictag. Tictag prides itself on providing data scientists with high quality dataset. With a 99.5% accuracy and a fast throughput, Tictag is able to keep up with fast paced developers to provide them with high quality datasets to power their machine learning models.

by Zaqi Silverano, Latasha Lenus, Tictag Tech Team